Multi-object detection and tracking in video sequences

Abstract : The work developed in this PhD thesis is focused on video sequence analysis. Thelatter consists of object detection, categorization and tracking. The development ofreliable solutions for the analysis of video sequences opens new horizons for severalapplications such as intelligent transport systems, video surveillance and robotics.In this thesis, we put forward several contributions to deal with the problems ofdetecting and tracking multi-objects on video sequences. The proposed frameworksare based on deep learning networks and transfer learning approaches.In a first contribution, we tackle the problem of multi-object detection by puttingforward a new transfer learning framework based on the formalism and the theoryof a Sequential Monte Carlo (SMC) filter to automatically specialize a Deep ConvolutionalNeural Network (DCNN) detector towards a target scene. The suggestedspecialization framework is used in order to transfer the knowledge from the sourceand the target domain to the target scene and to estimate the unknown target distributionas a specialized dataset composed of samples from the target domain. Thesesamples are selected according to the importance of their weights which reflectsthe likelihood that they belong to the target distribution. The obtained specializeddataset allows training a specialized DCNN detector to a target scene withouthuman intervention.In a second contribution, we propose an original multi-object tracking frameworkbased on spatio-temporal strategies (interlacing/inverse interlacing) and aninterlaced deep detector, which improves the performances of tracking-by-detectionalgorithms and helps to track objects in complex videos (occlusion, intersection,strong motion).In a third contribution, we provide an embedded system for traffic surveillance,which integrates an extension of the SMC framework so as to improve the detectionaccuracy in both day and night conditions and to specialize any DCNN detector forboth mobile and stationary cameras.Throughout this report, we provide both quantitative and qualitative results.On several aspects related to video sequence analysis, this work outperformsthe state-of-the-art detection and tracking frameworks. In addition, we havesuccessfully implemented our frameworks in an embedded hardware platform forroad traffic safety and monitoring.
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Ala Mhalla. Multi-object detection and tracking in video sequences. Computer Vision and Pattern Recognition [cs.CV]. Université Clermont Auvergne; Université du Centre (Sousse, Tunisie), 2018. English. ⟨NNT : 2018CLFAC084⟩. ⟨tel-02177037⟩

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