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This paper proposes an improvement to the multi-object tracking system framework based on the image inputs. By analyzing the role and performance of each block in the original multi-objects tracking system, the blocks of the original system are reconstructed to enhance the efficiency and yield a faster processing speed suiting the real-time applications. In the proposed method, the first two parts of the multi-object tracking system are merged into a single neural network designed for object detection and feature extraction. A new object association judgment method and JDE inspired prediction head are included in order to achieve a better and an outstanding association effect resulting in the overall improvement of the original system by 45.2&#x0025;. The enhanced method is aimed at the application of smart roadside units and uses fixed-viewpoint image input to achieve multi-object tracking on embedded platforms. The proposed method is implemented on the NVIDIA Jetson AGX Xavier embedded platform. The NVIDIA TensorRT software development kit is used to accelerate the neural network. The overall performance of the proposed system yields better efficiency compared to that of the original SDE design and the overall computing performance achieve up to 14&#x2013;26 images per second, making it ideal for the real-time smart roadside unit applications.

Real-Time Multiple Pedestrian Tracking With Joint Detection and Embedding Deep Learning Model for Embedded Systems