Multi-target Detection In CCTV Footage For Tracking Applications Using Deep Learning Techniques

Abstract:


Real-world CCTV footage often poses increased challenges in object tracking due to Pan-Tilt-Zoom operations, low camera quality and diverse working environments. Most relevant challenges are movingbackground, motion blur and severe scale changes. Convolutional neural networks, which offer state-of-the-art performance in object detection, are increasingly utilized to pursue a more efficient tracking scheme. In this work, the use of heterogeneous training data and data augmentation is explored to improve their detection rate in challenging CCTV scenes. Moreover, it is proposed to use the objects’ spatial transformation parameters to automatically model and predict the evolution of intrinsic camera parameters and accordingly tune the detector for better performance. The proposed approaches are tested on publicly available datasets and real-world CCTV videos.


  • A. Dimou, P. Medentzidou, F. Alvarez, P. Daras, "Multi-target Detection In CCTV Footage For Tracking Applications Using Deep Learning Techniques", IEEE International Conference on Image Processing, ICIP 2016, Sept 25-28, Phoenix, Arizona, USA

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    Contact Information

    Dr. Petros Daras, Principal Researcher Grade Α
    1st km Thermi – Panorama, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras@iti.gr