DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects

• URL: http://arxiv.org/abs/2407.09051v1
• Date: Fri, 12 Jul 2024 07:18:18 GMT
• Title: DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects
• Authors: Peng Wang, Yongcai Wang, Deying Li,
• Abstract summary: Multi-object tracking (MOT) on static platforms, such as surveillance cameras, has achieved significant progress. However, the effectiveness of traditional MOT methods is significantly reduced when it comes to dynamic platforms like drones. This paper proposes DroneMOT, which considers the fast movements of drones to enhance the drone-based object detection and feature embedding for small-sized, blurred, and occluded objects.
• Score: 6.449663756698312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking (MOT) on static platforms, such as by surveillance cameras, has achieved significant progress, with various paradigms providing attractive performances. However, the effectiveness of traditional MOT methods is significantly reduced when it comes to dynamic platforms like drones. This decrease is attributed to the distinctive challenges in the MOT-on-drone scenario: (1) objects are generally small in the image plane, blurred, and frequently occluded, making them challenging to detect and recognize; (2) drones move and see objects from different angles, causing the unreliability of the predicted positions and feature embeddings of the objects. This paper proposes DroneMOT, which firstly proposes a Dual-domain Integrated Attention (DIA) module that considers the fast movements of drones to enhance the drone-based object detection and feature embedding for small-sized, blurred, and occluded objects. Then, an innovative Motion-Driven Association (MDA) scheme is introduced, considering the concurrent movements of both the drone and the objects. Within MDA, an Adaptive Feature Synchronization (AFS) technique is presented to update the object features seen from different angles. Additionally, a Dual Motion-based Prediction (DMP) method is employed to forecast the object positions. Finally, both the refined feature embeddings and the predicted positions are integrated to enhance the object association. Comprehensive evaluations on VisDrone2019-MOT and UAVDT datasets show that DroneMOT provides substantial performance improvements over the state-of-the-art in the domain of MOT on drones.

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