Abstract:
Tracking-by-detection have become a hot topic for intelligent vehicle applications in recent year. Generally, the existing tracking-by-detection frameworks have difficult...Show MoreMetadata
Abstract:
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.
Published in: 2019 IEEE Intelligent Vehicles Symposium (IV)
Date of Conference: 09-12 June 2019
Date Added to IEEE Xplore: 29 August 2019
ISBN Information: