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Deep learning-based multiple moving vehicle detection and tracking using a nonlinear fourth-order reaction-diffusion based multi-scale video object analysis

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  • A novel automatic vehicle detection and tracking framework is proposed in this research work. First, the moving vehicles are detected in the frames of the video sequence by combining some deep learning and Gaussian Mixture Model-based object detection techniques. Then, the correspondence between the video objects detected in successive frames is determined using a multi-scale analysis of those objects. A scale-space representation is created by applying the numerical approximation algorithm that solves a nonlinear fourth-order reaction-diffusion based model whose mathematical validity is rigorously investigated here. A color image feature extraction is then performed at each scale using SURF and HOG-based features and the feature vectors determined at multiple scales are next concatenated into a final descriptor. A novel instance matching-based vehicle tracking technique using the distances between these feature vectors is then proposed. The results of the performed detection and tracking simulations are finally discussed.

    Mathematics Subject Classification: Primary: 35Axx, 68Txx; Secondary: 35A01, 35A02, 49K40, 62H30, 68T07, 68U10, 94A08, 62M40, 65D18.


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  • Figure 1.  Example of moving vehicle detection process

    Figure 2.  Architecture of the vehicle detection and tracking framework

    Figure 3.  Moving vehicle tracking example

    Table 1.  Moving vehicle detection method comparison

    Detection technique Precision Recall
    The proposed approach 0.8423 0.8257
    Aggregated Channels Network (ACF) 0.6741 0.6824
    Gaussian Mixture Models (GMM) 0.6843 0.6722
    Faster R-CNN 0.8192 0.8192
    R-CNN 0.6581 0.6625
    YOLO-v2 0.7632 0.7811
    Frame differencing (FD) 0.5826 0.5972
    HOG + SVM 0.6149 0.6349
     | Show Table
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    Table 2.  Multiple vehicle tracking method comparison

    Tracking approach Precision Recall
    The proposed technique 0.8401 0.8216
    GMM + Mean-shift tracking 0.7148 0.7332
    GMM + Kalman filter 0.7643 0.7527
    EB + IoU tracker 0.8576 0.8615
    SIFT tracking 0.6233 0.6181
    SIFT + Kalman filtering 0.7486 0.7391
    Frame differencing (FD)+object matching 0.5801 0.5879
    HOG + SVM + feature matching 0.6102 0.6213
     | Show Table
    DownLoad: CSV
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