Tackling Occlusion in Siamese Tracking with Structured Dropouts
Occlusion is one of the most difficult challenges in object tracking to model. This is because unlike other challenges, where data augmentation can be of help, occlusion is hard to simulate as the occluding object can be anything in any shape. In this paper, we propose a simple solution to simulate the effects of occlusion in the latent space. Specifically, we present structured dropout to mimick the change in latent codes under occlusion. We present three forms of dropout (channel dropout, segment dropout and slice dropout) with the various forms of occlusion in mind. To demonstrate its effectiveness, the dropouts are incorporated into two modern Siamese trackers (SiamFC and SiamRPN++). The outputs from multiple dropouts are combined using an encoder network to obtain the final prediction. Experiments on several tracking benchmarks show the benefits of structured dropouts, while due to their simplicity requiring only small changes to the existing tracker models.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationObject TrackingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SiamReID: Confuser Aware Siamese Tracker with Re-identification Feature
Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are p…
ObjectObject TrackingTripletObject Tracking Using Siamese Network-Based Reinforcement Learning
Object tracking is a technique for tracking a specific object appearing in a video sequence while observing its features or changes. Recently, many algorithms showing high performance have emerged by applying the Siamese…
ObjectObject Trackingreinforcement-learningReinforcement Learning+1F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking
This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D sin…
3D Single Object TrackingObjectObject TrackingT-SiamTPN: Temporal Siamese Transformer Pyramid Networks for Robust and Efficient UAV Tracking
Aerial object tracking remains a challenging task due to scale variations, dynamic backgrounds, clutter, and frequent occlusions. While most existing trackers emphasize spatial cues, they often overlook temporal dependen…
Computational EfficiencyObject TrackingSMMT: Siamese Motion Mamba with Self-attention for Thermal Infrared Target Tracking
Thermal infrared (TIR) object tracking often suffers from challenges such as target occlusion, motion blur, and background clutter, which significantly degrade the performance of trackers. To address these issues, this p…
MambaObject Tracking