RFID-assisted visual multiple object tracking without using visual appearance and motion

Visual Multiple Object Tracking (MOT) typically utilizes appearance and motion clues for associations. However, these features may be limited under certain challenging scenarios, such as appearance ambiguity and frequent occlusions. In this paper, we introduce a novel deep RF-affinity neural network (DRFAN) that enhances visual tracking with the aid of a passive wireless positioning device, Radio Frequency Identification (RFID). DRFAN aims to solve object tracking by introducing a new concept of a "candidate trajectory" to indicate target movement. This approach fundamentally deviates from existing fusion methods that rely on known visual tracks. Instead, DRFAN exclusively uses detection bounding boxes and RFID signals. The proposed method overcomes the limitations of visual tracking by swiftly resuming correct tracking whenever a failure occurs. This is the first time using signals from low-cost passive RFID tags to achieve image-level localization, and a discriminative neural network is designed specifically for RFID-assisted visual association. Our experimental results validate the robustness and applicability of the proposed approach.