SiamMT: Real-Time Arbitrary Multi-Object Tracking

Visual object tracking preserves the identity of an object throughout a video. Real applications often demand systems capable of tracking multiple objects in real time, but multi-object tracking solutions usually follow tracking-by-detection, depending on costly detectors and category-specific training. SiamMT efficiently applies individual visual tracking techniques to multiple arbitrary objects in real time, making it a deep-learning-based arbitrary multi-object tracker. It uses global frame feature extraction with a fully convolutional neural network, crops and resizes object search areas, and performs the final similarity operation between search areas and target exemplars with optimized pairwise cross-correlation. These components allow the system to track multiple targets in a scalable manner.