Improving Multi Object Tracking-By-Detection Model Using a Temporal Interlaced Encoding and a Specialized Deep Detector
Ala Mhalla, Thierry Château · 2019
Tracking-by-detection have become a hot topic for intelligent vehicle applications in recent year. Generally, the existing tracking-by-detection frameworks have difficulties with congestion, occlusion, and inaccurate detection in crowded scenes. In this paper, we propose a new framework for Multi-Object Tracking-by-detection (MOT-TbD) based on an temporal interlaced encoding video model and a specialized Deep Convolutional Neural Network (DCNN) detector. Spatio-temporal variation of objects between images are encoded into “interlaced images”. A specialized “interlaced object” deep detector is trained on a interlaced dataset. The detected objects are associated with a classical data association algorithm. Since interlaced objects are built to increase overlap during the association step, the performance of the MOT-TbD increases related to the same detector/association algorithm applied on non-interlaced images. The effectiveness of the method is demonstrated by experiments on popular tracking-by-detection datasets such as the PETS 2009 and TUD. Experimental results show that the proposed framework outperforms several state-of-the-art MOT-TbD methods.