Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

Detection of weather conditions around the car

Problematika kojom se bavi diplomski rad je detekcija vremenskih uvjeta u okolini automobila. U prvom dijelu diplomskog rada opisan je problem detekcije vremenskih uvjeta, kao i neki dosadašnji radovi koji se bave sličnim problemima. Drugim dijelom rada predstavljena je teorijska osnova potrebna za rješavanje praktičnog dijela rada. Rješenje praktičnog dijela rada, kao i rezultati testiranja rješenja opisani su u trećem dijelu diplomskog rada. Za potrebe treniranja klasifikatora izrađeni su skupovi podataka za treniranje i validaciju iz dva izvora: BDD100K baze podataka i izvlačenjem okvira iz prikupljenih video sekvenci. Kreirane su dvije baze podataka: WDC_dataset i DNC_dataset baza podataka, gdje je WDC_dataset korištena pri učenju klasifikatora koji bi trebao raspoznavati vremenske uvjete, a DNC_dataset pri učenju klasifikatora koji bi trebao raspoznavati vrijeme dana, odnosno dan i noć. Predloženo rješenje je realizirano korištenjem konvolucijske neuronske mreže s tri konvolucijska sloja između kojih su korištene ReLu aktivacijske funkcije i slojevi sažimanja po maksimalnoj vrijednosti. Rješenje bazirano na konvolucijskoj neuronskoj mreži je uz korištenje empirijski određenog praga i kružnog međuspremnika za dodatno filtriranje loših predikcija pokazalo zadovoljavajuće rezultate nad testnim video sekvencama. Dobiveno rješenje postiže točnost od 98.3% nad testnim video sekvencama, gdje najbolje rezultate postiže kod detekcije snježnog vremena (100%), a najlošije kod detekcije maglovitog vremena (97.25%).This diploma thesis details the detection of weather conditions in the car environment. The first part of the thesis describes the problem of weather detection, as well as some previous work dealing with same or similar problems. The second part of the paper presents the theoretical basis for dealing with the practical part of the paper. The solution of the practical part of the thesis, as well as the results of testing the solution are described in the third part of the thesis. For training purposes, training and validation data sets were created from two main sources: the BDD100K database and by extracting frames from the collected video sequences. Two databases were created: WDC_dataset database and DNC_dataset database, where WDC_dataset was used for weather conditions classifier training, and DNC_dataset for day-night classifier training. The proposed solution was implemented using convolutional neural network with three convolutional layers, between which ReLu activation functions and max pooling layers were used. The solution based on the convolutional neural network is using a empirically determined threshold and a circular buffer to further filter out false predictions. The proposed solution achieved an accuracy of 98.3% for tested video sequences, where the solution is showing the best results in snowy weather detection (100%) and worst in foggy weather detection (97.25%)

Plan de reordenamiento vehicular para reducir el congestionamiento en un distrito de la provincia de Chiclayo

Se tiene conocimiento que el servicio de transporte se ha vuelto indispensable en el día a día para la población alrededor del mundo, más aún con el crecimiento demográfico en las diversas ciudades y en el caso de la presente investigación, la población de un distrito de la provincia de Chiclayo. Sin embargo, esta necesidad de poder trasladarse en las llamadas horas punta genera el congestionamiento vehicular. En la actualidad, somos testigos de la aglomeración de vehículos en distintos puntos de la ciudad, por lo que, en esta investigación, se tiene como objetivo general; proponer un plan de reordenamiento vehicular para reducir el congestionamiento. La investigación fue básica, de enfoque cuantitativo y área transversal. Para la recolección de datos se trabajó a través de un cuestionario, cuyos resultados han sido procesados a través de software estadístico SPSS. Se concluye que el tráfico vehicular es una problemática común en el área estudiada, tanto el transporte público como la infraestructura vial se ven afectados por el congestionamiento, lo que genera impactos negativos en la movilidad de las personas. Finalmente, con el plan de reordenamiento vehicular evaluado se puede optar por aplicarlo y así disminuir el problema del congestionamiento vehicular