This paper presents a multi-vehicle tracking algorithm using appearance feature and motion history based on heterogeneous deep learning aimed at autonomous driving applications. Our proposed multi-vehicle tracking model follows the tracking-by-detection paradigm. To track multiple vehicles, we utilize the appearance and motion features of the target vehicles in consecutive frames. The proposed multi-vehicle tracking system employs a deep convolutional neural network, which is trained with a triplet loss minimization method to extract appearance features. The key contribution of the proposed method lies in a Long Short-Term Memory (LSTM) with a fully connected layer that accurately predicts the probability distribution of the next appearance and motion features of tracked objects. We constructed a multi-vehicle tracking dataset from various real road traffic using a camera sensor on a vehicle. To evaluate our proposed algorithm, we use several multi-target tracking datasets from the KITTI object tracking benchmark, which is a Public tracking dataset, as well as our evaluation dataset. Experimental results demonstrate that the proposed multi-vehicle tracking algorithm achieves a MOTA of 84.5% and MOTP 86.3% on the KITTI tracking dataset, and a MOTA of 81.8% and MOTP 84.8% on our evaluation dataset, an improvement of 8.6% and 9.6% over the previous methods.
Multi-Vehicle Tracking Using Heterogeneous Neural Networks for Appearance And Motion Features
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 20 , 3 ; 720-733
2022-12-01
14 pages
Article (Journal)
Electronic Resource
English
Convolution Neural network (CNN) , KL divergence , Multiple Object Tracking (MOT) , Object detection , Triplet loss , Vehicle tracking Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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