Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Human activity recognition using a wearable camera

Tesi en modalitat cotutela Universitat Politècnica de Catalunya i Queen Mary, University of London. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA n° 2010-0012]Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad.Postprint (published version

Selection practices and evaluation of growth and composition in three lines of synthetic beef cattle

Data in the present study came from two separate beef cattle breeding projects at Iowa State University. Data set-I included growth and carcass information of progeny in the small, medium, and large lines of synthetic cattle. Progeny were born during the years 1978 through 1990 at Rhodes and McNay research farms. Data were used to evaluate selection practices in the three synthetic lines, to evaluate effects of some crossbreeding parameters on carcass traits, and to estimate genetic parameters and genetic trends for carcass traits. Data set-II included carcass and serially measured live-animal traits collected over a 6-year period (1991--1996). Most of the data came from progeny of purebred Angus and Simmental sires with known expected progeny difference and synthetic females from a previous project. Data set-II was used to study effects of sex and breed on growth and composition of feedlot cattle and to determine the best strategy to adjust serially measured traits to a constant age end point. The overall mean generation interval was 4.11 years. When averaged by line, 1.82, 1.47, and 1.28 generations of selection was made in the small, medium, and large lines, respectively. Mean actual sire index differentials per generation were, 1.28, -.47, and .84sigma for the small, medium, and large lines, respectively. There was a significant (P \u3c .05) difference in direct additive effect between Jersey, Angus, and the Simmental breeds for most of the carcass traits considered. However, differences in breed maternal, average individual heterosis, and average maternal heterosis were not different from zero (P \u3e .10). Heritability of hot carcass weight, dressing percentage, longissimus muscle area, fat thickness, and percentage of kidney, pelvic, and heart fat in the small line were, .30, .09, .21, .34, and. 15, respectively. The respective values in the medium line were, .52, .35, .33, .29, and .07. Heritability values in the large line were in the order of .31, .18, .17, .31, and .18, respectively. Sire selection based on weaning indices showed a significant (P \u3c .05) genetic change for some of the carcass traits. It was concluded that index equations designed to improve beef carcasses need to incorporate carcass information in an index. Analysis of serially measured fat thickness, longissimus muscle area, body weight, hip height, and ultrasound percentage intramuscular fat showed a limitation in the use of growth models based on pooled data. Therefore, it was concluded that regression parameters from a within-animal regression of a serially measured trait on age, averaged by sex and breed, are the best choice in describing growth and adjusting data to a constant age end point

The Advertisement Practice and Audience Reaction towards it: The case of Oromia Television.

The main aim of the study was to examine the advertisement practices of Oromia Television and audience reaction with the theoretical foundation of encoding and decoding and situational ethical theory. The study of the research employed descriptive design involving both quantitative and qualitative approaches.   Data were qualitatively collected through in-depth interview and questionnaire with open and close ended questions. Besides, purposive sampling method was employed to select the respondents of the study. The data obtained from conducted interview were analyzed qualitatively.  In the study, the data which were gathered through document review basically ethical guideline and sample of broadcast ads, were presented in the forms of descriptive and the responses of the audience were presented in the form of the tables and chart with frequency and percentage. Finally, this data were analyzed quantitatively.  Through the help of above methods the research answered four basic research questions.   The findings of the study revealed that Oromoia Television advertisement manual which has produced by Oromoia Television and which were not given more attention to the problem (Marketing concept and professionalism) of advertisement ethics. The procedures more of followed by conducting advertisement focused on revenue generation. And the findings of the study also revealed that most of ads have the problem of imitation from local and foreign language, this leads the advertisement practices of Oromoia Television to standardization, similarly the practices are not understandable, the advertisement message is not adequate and some advertisement messages have no logical link with the product or products being communicated and advertisements transmitted on Oromoia Television are unreliable, exaggerated and deceptive information. Based on these indications the study concludes that there are problems of ethics in Oromoia Television advertisement which have been resulted from many factors. Oromoia Television advertisement practices have negative influence on purchasing decisions of the audiences. Finally, great attention should be given for improvement of the advertisement practices of Oromoia Television

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Effect of Soil Incorporated Pruned Pigeon pea and Nitrogen on System Productivity in Maize/Pigeon pea Intercropping

አህፅሮትበቆሎን በተመሳሳይ ማሣ ላይ አከታትሎ በማምረት የሚከሰተውን የምርት ማሽቆልቆል ገለባ ማሳ ላይ በማስቀረትና የተወሰነ የአፈር ማዳበሪያ በመጨመር መቀነስ ይቻላል፡፡ የዚህ ጥናት ዓላማ በቆሎንና የርግብ አተርን አሰባጥሮ በመዝራትና የርግብ አተርን ቅርንጫፍ ገርዞ ከአፈር ጋር በማወሃድና የናይትሮጂንን መጠን በመቀነስ በሰብል ምርትና በአፈር ንጥረ ነገር ላይ ያለውን ተፅዕኖ መለየት ነው፡፡ ጥናቱ የርግብ አተርን ቅርንጫፍ በመግረዝ ከላይ 0፤2፤4፤6 በማስቀረትና 18፤41፤64፤87፤110 ኪ.ግ. በሄክታር ናይትሮጂን በመጨመር በበኮ ግብርና ምርምር ማዕከል በ2005፤2006፤2007 ዓ.ም. ተከናወነ፡፡ በቆሎንና የርግብ አተርን አሰባጥሮ በመዝራት ታችኞቹን የርግብ አተር ቅርንጫፍ በመግረዝ 2 የላይኞቹን ማስቀረት ብቻውን ከተዘራው በቆሎ ጋር ሲነፃፀር የበቆሎ ምርትን 8% ሲጨምር ተጨማሪ ርግብ አተር 972 ኪ.ግ. በሄክታር አስገኝቷል፡፡ ይህ አሠራር አሲዳማነትን በመቀነስ የአፈርን ንጥረ ነገር ከመጨመሩም በላይ እነዚህን ሁለት ሰብሎች ለየብቻ ለመዝራት ይፈለግ ነበረውን ተጨማሪ 0.42 ሄክታር በማስቀረት ለበቆሎ ይጨመር የነበረውን ናይትሮጂን በመቀነስ ምርትን መጨመሩ ተረጋግጧል፡፡AbstractDecline of return in maize monoculture requires amendment of nutrients removed from the soil through retention of biomass on the soil with some addition of inorganic fertilizers. This study was executed for three consecutive years (2013-2015)to evaluate the effect of pruning levels while leaving the upper (0, 2, 4 and 6) parts of perennial pigeon pea and N levels (18, 41, 64, 87 and 110 kg ha-1) on yields of component crops and on some soil nutrients in maize/pigeon pea intercropping. The result indicated that the main effects due to pruning of pigeon pea and incorporation in to the soil and N level were significant for maize biomass weight during 2013 and 2014 and for maize grain yield throughout the experimental periods. Pruning of lower branches of pigeon pea while leaving the upper 2 in maize/pigeon pea intercropping increased grain yield of maize by 8% compared to the sole maize monocropping and produced a mean pigeon pea grain yield of 972 kg ha-1. It also reduced soil acidity, increased soil organic carbon, total N and available P compared to the sole maize monoculture. The highest LER of 1.42 and the highest net benefit of Birr 32,347 ha-1 were also obtained due to pruning of pigeon pea while leaving the upper 2 and incorporating in to the soil in intercropping of maize/pigeon pea at reduced N level. This branch management at reduced N level is recommended for the high productivity and reduced resource use efficiency for sub-humid areas of Bako

Multiple Advantages of Pigeon Pea (Cajanas Cajan) in Maize Based Cropping Systems: Used as Live Stake for Climbing Bean with Phosphorus Rates and Maize Productivity Enhancement in Mono Cropping Areas

Continuous maize based monoculture is one of the major bottlenecks limiting land and crop productivity in western Ethiopia. Pigeon pea plays vital role in rehabilitating degraded land and depleted soils due to its high N-fixation capacity, high biomass production and high litter fall. It can also support climbing bean as live stake. Two sets of the experiment were conducted for five consecutive years at Bako Agricultural Research Center. In 2009 and 2010 cropping seasons, pigeon pea was established and two crops (tef and finger millet) which were considered as one factor were under sown as tangua systems until the pigeon pea reaches its maximum growth. In 2010 and 2011, climbing bean was planted in established pigeon pea under different pruning options ( 25%,50% and 75% branch remaining) and with P2O5 rates (0,15,30 and 46 kg/ha). Thus treatments were arranged in factorial combinations and replicated three times. In 2011 and 2012, maize was planted on the permanent plots that two crops sown during pigeon pea establishment and received pigeon pea biomass under different pruning options  and with N application rates (0, 36, 72 and 110 kg/ha) and designed in RCBD factorial arrangement. The result revealed that better biomass and grain yield of finger millet under sown during the establishment of pigeon pea was obtained compared to tef.  Significant yield increase of climbing bean was recorded when percentage of pigeon pea branch removal was increased. Application of phosphorus increased grain yield of climbing bean, but there was no significant difference on yield of pigeon pea. Seasonal variability highly affected maize yield performance and the yield was highly reduced during 2012 compared to 2011 due to the lowest annual rainfall amount received in this season against the last ten years. There was strong and positive correlation of maize yield and annual rainfall of cropping season. Maize yield was not significantly different due to the residual effects of pigeon pea biomass retained under different pruning levels. But, highly significant yield increase was observed due to residual effects of retained biomass as compared to farmers’ practices and even under maize-climbing bean intercropping. Application of N  to maize planted on previous plots that received pigeon pea biomass showed no significant variations though the  better yield was recorded when 33 kg/ha N and 72 kg/ha N were applied in 2012 and 2011 cropping seasons, respectively. However, significant yield increases were obtained when the crop was planted on previous plots that were retained by pigeon pea biomass, regardless of N application rates, compared to the sole maize monoculture and in intercropping system. The result also clarifies the performance of maize without N application gave similar grain yield compared to current farmers’ practices. Generally, significant yield increment by 6-17% and 5-30% over farmers’ practices were recorded in 2011 and 2012, respectively. In short rainy season, maize yield planted on previous plots retained by pigeon pea biomass or as litter fall and with no N performed significantly better than farmers’ practices. Retention of pigeon pea biomass or released as litter fall on the following year for maize production can also significantly reduce 66-100% of the total recommended N while significantly increase maize yield. In 2013, the maize was planted on the permanent plots that had pigeon biomass or litter fall and with no chemical fertilizers revealed more than 100% and 75% yield increments as compared to yield of maize under intercrops and farmers’ practices. Indeed, pigeon pea can be used as live stake for climbing bean production or pigeon pea-climbing bean intercropping at appropriate pruning level (up to 50% to 75% branch removals). Moreover, the buildup of soil fertility through establishing pigeon pea and its biomass retention evidently boost the productivity of the soil and even 100% reduction of chemical N fertilizer cost. Hence farmers are advised not to apply any N fertilizer sources in the following years since its left over effects significantly enhance maize yield. However, further investigation is needed to specify the frequency of organic matter buildup using this pigeon pea plant and its impact on availability of naturally fixed nutrients, likes phosphorus. Keywords: Pigeon pea, Climbing bean, Pruning levels, Nitrogen, Phosphoru

Determinants of financial performance : evidence from Ethiopia insurance companies

The objective of this research was identifying the determinants of financial performance in case of Ethiopian Insurance Companies over the period of 2010-2015. Profitability ratios were used as proxy of financial performance measurement; return of asset (ROA) and return of equity (ROE). Panel data set from nine insurance companies over the period of six years were used. The descriptive statistics implied that nonexistence of variation in ROA and ROE since the standard deviation statistics for ROA (34%) and ROE (11%) were below the respective means (63% and 19%). To identify the determinants of financial performance, Ordinary least squire (OLS) estimation method was employed. The estimation result showed that capital adequacy, liquidity, size, age, loss, leverage were the key determinants of financial performance. From this researchers concluded that financial performance mainly driven by firm specific factors. Thus, attention should be given to firm specific variables to have a sound financial performance.peer-reviewe

In vitro evaluation of marker assisted conversion of adapted sorghum varieties into Striga hermonthica resistant versions

Striga has long been recognized to infest staple food crops like sorghum in Ethiopia. This study was designed to introgress Striga-resistance genes into popular and farmer-preferred varieties through marker-assisted backcrossing and to assess resistance based on Striga germination stimulant activity inagar-gel assay (aga). The experiment was arranged in completely randomized design with four replications. Genotypes performance, heritability and genetic advance were analyzed and Germination rate was measured. The progeny showed significant genetic variation for maximum germination distance (mgd), germination rate (gr), and germination index (gi). The mean mgd ranged from 0.0 mm to 29.45 mm and gr ranged from 0.0% to 72.38%.Of the 118 backcrossed lines, 22.9% showed less than 10 mm of mgd and gr of <30%, revealing provision of low germination stimulant/strigolactones production (lgs). There were significant positive (r = 0.4-0.81) correlations showing the roles of these parameters as selection criteria in breeding for resistance. The existence of higher heritability (h2b = 77-83%) and genetic advance (ga = 62-93%) for the germination parameters indicated possibilities for improving resistance against Striga through selection. Genotypes that carry different qtls showed different capacity of producing Striga germination stimulants in the aga. The combined effect of two qtls (lgs2_SBI-05_60404021 and lgs_3_60629027) at a time showed lower Striga germination stimulant activity and better field resistance indicating existence of possible cumulative effects. Thus, the study showed that marker-assisted backcrossing for transfer of lgs qtls from donor into popular and farmers preferred cultivars has the potential to enhance tolerance/resistance to Striga in sorghum

Human activity recognition using a wearable camera

PhDAdvances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first-person videos. The proposed features encode discriminant characteristics from magnitude, direction and dynamics of motion estimated using optical flow. Moreover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variations of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classifiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from firstperson videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiple motion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activities. Furthermore, we apply cross-domain knowledge transfer between inertial-based and vision-based approaches for egocentric activity recognition. We propose sparsity weighted combination of information from different motion modalities and/or streams. Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity