Abstract:

This paper established a multiple-object tracking algorithm for autonomous-vehicles integrated with an attention mechanism to improve the tracking accuracy. The multiple-object tracking algorithm extracted object appearance features by using YOLOv3 neural network with attention mechanism that enhanced the performance of feature extraction network. The algorithm extracted discriminative features of the target or the background. A Long Short-Term Memory (LSTM) network extracted the target motion features, while the object tracking algorithm modeled the target trajectory dynamically. The algorithm accomplished a multiple object tracking through data matching and association based on tracked target similarity degree. Experiments were done on a multiple object tracking dataset MOT16. The results show that the object detection success rate increases 1.9% compared with YOLOv3 network, using the object detection algorithm with attention mechanism with a tracking accuracy of 53.9% and a tracking precision of 79.0%. Therefore, the algorithm achieves a stable target tracking.

Key words: autonomous-vehicles, multiple-object tracking algorithm, deep neural networks, attention mechanism