Assessing machine learning classifiers for the detection of animals' behavior using depth-based tracking

[EN] There is growing interest in the automatic detection of animals' behaviors and body postures within the field of Animal Computer Interaction, and the benefits this could bring to animal welfare, enabling remote communication, welfare assessment, detection of behavioral patterns, interactive and adaptive systems, etc. Most of the works on animals' behavior recognition rely on wearable sensors to gather information about the animals' postures and movements, which are then processed using machine learning techniques. However, non-wearable mechanisms such as depth-based tracking could also make use of machine learning techniques and classifiers for the automatic detection of animals' behavior. These systems also offer the advantage of working in set-ups in which wearable devices would be difficult to use. This paper presents a depth-based tracking system for the automatic detection of animals' postures and body parts, as well as an exhaustive evaluation on the performance of several classification algorithms based on both a supervised and a knowledge-based approach. The evaluation of the depth -based tracking system and the different classifiers shows that the system proposed is promising for advancing the research on animals' behavior recognition within and outside the field of Animal Computer Interaction. (C) 2017 Elsevier Ltd. All rights reserved.This work is funded by the European Development Regional Fund (EDRF-FEDER) and supported by Spanish MINECO with Project TIN2014-60077-R. It also received support from a postdoctoral fellowship within the VALi+d Program of the Conselleria d'Educacio, Cultura I Esport (Generalitat Valenciana) awarded to Alejandro Catala (APOSTD/2013/013). The work of Patricia Pons is supported by a national grant from the Spanish MECD (FPU13/03831). Special thanks to our cat participants and their owners, and many thanks to our feline caretakers and therapists, Olga, Asier and Julia, for their valuable collaboration and their dedication to animal wellbeing.Pons Tomás, P.; Jaén Martínez, FJ.; Catalá Bolós, A. (2017). Assessing machine learning classifiers for the detection of animals' behavior using depth-based tracking. Expert Systems with Applications. 86:235-246. https://doi.org/10.1016/j.eswa.2017.05.063S2352468

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Towards Intelligent Playful Environments for Animals based on Natural User Interfaces

Tesis por compendioEl estudio de la interacción de los animales con la tecnología y el desarrollo de sistemas tecnológicos centrados en el animal está ganando cada vez más atención desde la aparición del área de Animal Computer Interaction (ACI). ACI persigue mejorar el bienestar de los animales en diferentes entornos a través del desarrollo de tecnología adecuada para ellos siguiendo un enfoque centrado en el animal. Entre las líneas de investigación que ACI está explorando, ha habido bastante interés en la interacción de los animales con la tecnología basada en el juego. Las actividades de juego tecnológicas tienen el potencial de proveer estimulación mental y física a los animales en diferentes contextos, pudiendo ayudar a mejorar su bienestar. Mientras nos embarcamos en la era de la Internet de las Cosas, las actividades de juego tecnológicas actuales para animales todavía no han explorado el desarrollo de soluciones pervasivas que podrían proveerles de más adaptación a sus preferencias a la vez que ofrecer estímulos tecnológicos más variados. En su lugar, estas actividades están normalmente basadas en interacciones digitales en lugar de explorar dispositivos tangibles o aumentar las interacciones con otro tipo de estímulos. Además, estas actividades de juego están ya predefinidas y no cambian con el tiempo, y requieren que un humano provea el dispositivo o la tecnología al animal. Si los humanos pudiesen centrarse más en su participación como jugadores de un sistema interactivo para animales en lugar de estar pendientes de sujetar un dispositivo para el animal o de mantener el sistema ejecutándose, esto podría ayudar a crear lazos más fuertes entre especies y promover mejores relaciones con los animales. Asimismo, la estimulación mental y física de los animales son aspectos importantes que podrían fomentarse si los sistemas de juego diseñados para ellos pudieran ofrecer un variado rango de respuestas, adaptarse a los comportamientos del animal y evitar que se acostumbre al sistema y pierda el interés. Por tanto, esta tesis propone el diseño y desarrollo de entornos tecnológicos de juego basados en Interfaces Naturales de Usuario que puedan adaptarse y reaccionar a las interacciones naturales de los animales. Estos entornos pervasivos permitirían a los animales jugar por si mismos o con una persona, ofreciendo actividades de juego más dinámicas y atractivas capaces de adaptarse con el tiempo.L'estudi de la interacció dels animals amb la tecnologia i el desenvolupament de sistemes tecnològics centrats en l'animal està guanyant cada vegada més atenció des de l'aparició de l'àrea d'Animal Computer Interaction (ACI) . ACI persegueix millorar el benestar dels animals en diferents entorns a través del desenvolupament de tecnologia adequada per a ells amb un enfocament centrat en l'animal. Entre totes les línies d'investigació que ACI està explorant, hi ha hagut prou interès en la interacció dels animals amb la tecnologia basada en el joc. Les activitats de joc tecnològiques tenen el potencial de proveir estimulació mental i física als animals en diferents contextos, podent ajudar a millorar el seu benestar. Mentre ens embarquem en l'era de la Internet de les Coses, les activitats de joc tecnològiques actuals per a animals encara no han explorat el desenvolupament de solucions pervasives que podrien proveir-los de més adaptació a les seues preferències al mateix temps que oferir estímuls tecnològics més variats. En el seu lloc, estes activitats estan normalment basades en interaccions digitals en compte d'explorar dispositius tangibles o augmentar les interaccions amb estímuls de diferent tipus. A més, aquestes activitats de joc estan ja predefinides i no canvien amb el temps, mentre requereixen que un humà proveïsca el dispositiu o la tecnologia a l'animal. Si els humans pogueren centrar-se més en la seua participació com a jugadors actius d'un sistema interactiu per a animals en compte d'estar pendents de subjectar un dispositiu per a l'animal o de mantenir el sistema executant-se, açò podria ajudar a crear llaços més forts entre espècies i promoure millors relacions amb els animals. Així mateix, l'estimulació mental i física dels animals són aspectes importants que podrien fomentar-se si els sistemes de joc dissenyats per a ells pogueren oferir un rang variat de respostes, adaptar-se als comportaments de l'animal i evitar que aquest s'acostume al sistema i perda l'interès. Per tant, esta tesi proposa el disseny i desenvolupament d'entorns tecnològics de joc basats en Interfícies Naturals d'Usuari que puguen adaptar-se i reaccionar a les interaccions naturals dels animals. Aquestos escenaris pervasius podrien permetre als animals jugar per si mateixos o amb una persona, oferint activitats de joc més dinàmiques i atractives que siguen capaces d'adaptar-se amb el temps.The study of animals' interactions with technology and the development of animal-centered technological systems is gaining attention since the emergence of the research area of Animal Computer Interaction (ACI). ACI aims to improve animals' welfare and wellbeing in several scenarios by developing suitable technology for the animal following an animal-centered approach. Among all the research lines ACI is exploring, there has been significant interest in animals' playful interactions with technology. Technologically mediated playful activities have the potential to provide mental and physical stimulation for animals in different environmental contexts, which could in turn help to improve their wellbeing. As we embark in the era of the Internet of Things, current technological playful activities for animals have not yet explored the development of pervasive solutions that could provide animals with more adaptation to their preferences as well as offering varied technological stimuli. Instead, playful technology for animals is usually based on digital interactions rather than exploring tangible devices or augmenting the interactions with different stimuli. In addition, these playful activities are already predefined and do not change over time, while they require that a human has to be the one providing the device or technology to the animal. If humans could focus more on their participation as active players of an interactive system aimed for animals instead of being concerned about holding a device for the animal or keep the system running, this might help to create stronger bonds between species and foster better relationships with animals. Moreover, animals' mental and physical stimulation are important aspects that could be fostered if the playful systems designed for animals could offer a varied range of outputs, be tailored to the animal's behaviors and prevented the animal to get used to the system and lose interest. Therefore, this thesis proposes the design and development of technological playful environments based on Natural User Interfaces that could adapt and react to the animals' natural interactions. These pervasive scenarios would allow animals to play by themselves or with a human, providing more engaging and dynamic playful activities that are capable of adapting over time.Pons Tomás, P. (2018). Towards Intelligent Playful Environments for Animals based on Natural User Interfaces [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/113075TESISCompendi

Machine Analysis of Facial Expressions

No abstract

Quick, accurate, smart: 3D computer vision technology helps assessing confined animals' behaviour

<p>(a) Visual representation of the alignment of two sequences using the Dynamic Time Warping (DTW). The DTW stretches the sequences in time by matching the same point with several points of the compared time series. (b) The Needleman Wunsh (NW) algorithm substitutes the temporal stretch with gap elements (red circles in the table) inserting blank spaces instead of forcefully matching point. The alignment is achieved by arranging the two sequences in this table, the first sequence row-wise (T) and the second column-wise (S). The figure shows a score table for two hypothetical sub-sequences (i, j) and the alignment scores (numbers in cells) for each pair of elements forming the sequence (letters in head row and head column). Arrows show the warping path between the two series and consequently the final alignment. The optimal alignment score is in the bottom-right cell of the table.</p

Automatic Monitoring of dairy cows’ lying behaviour using a computer vision system in open barns

Received: January 31st, 2023 ; Accepted: April 9th, 2023 ; Published: April 27th, 2023 ; Correspondence: [email protected] Livestock Farming offers opportunities for automated, continuous monitoring of animals, their productivity, welfare and health. The video-based assessment of animal behaviour is an automated, non-invasive and promising application. The aim of this study is to identify possible parameters in dairy cows’ lying behaviour that are the basis for a holistic computer vision-based system to assess animal health and welfare. Based on expert interviews and a literature review, we define parameters and their optimum in form of gold standards to evaluate lying behaviour automatically. These include quantitative parameters such as daily lying time, lying period length, lying period frequency and qualitative parameters such as extension of the front and hind legs, standing in the lying cubicles, or total lateral position. The lying behaviour is an example within the research context for the development of a computer vision-based tool for automated detection of animal behaviour and appropriate housing design

An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification

This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7