Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking
This addresses the need for simpler, more efficient tracking systems in computer vision applications like ADAS, though it appears incremental.
The paper tackles the problem of high complexity and many hyperparameters in multi-object tracking for ADAS by introducing an Enhanced Siamese Neural Network system that uses similarity mapping with appearance and geometric information, achieving competitive speed and accuracy on the MOT16 benchmark.
Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS). Despite growing attention, achieving high performance tracking is still challenging, with state-of-the- art systems resulting in high complexity with a large number of hyper parameters. In this paper, we focus on reducing overall system complexity and the number hyper parameters that need to be tuned to a specific environment. We introduce a novel tracking system based on similarity mapping by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance and geometric information, and is trainable end-to-end. Our system achieves competitive performance in both speed and accuracy on MOT16 challenge, compared to known state-of-the-art methods.