NullSpaceRDAR: Regularized discriminative adaptive nullspace for object tracking

Abstract

Recently, discriminative-based and Siamese-based trackers have achieved outstanding performance on most tracking benchmarks. However, these trackers use the pre-trained backbone networks that have been mainly designed for classification to extract target-specific features without taking into consideration the visual object tracking task. In this paper, we propose NullSpaceRDAR, a novel tracker that learns a robust target-specific feature representation specifically designed for object tracking. This feature representation is learned by projecting the traditional backbone feature space onto a novel discriminative nullspace that is used to regularize the backbone loss function. We refer to the discriminative nullspace herein as joint-nullspace. The same target features (i.e., target-specific) in the proposed joint-nullspace are collapsed into a single point, and different target-specific features are collapsed into different points. Consequently, the joint-nullspace forces the network to be sensitive to the object’s variations from the same class (i.e., intra-class variations). Moreover, a modified adaptive loss function is developed for bounding box estimation to select the most suitable loss function from a super set family of loss functions based on the training data. This makes NullSpaceRDAR more robust to different challenges such as occlusions and background clutter. Extensive experiments have been conducted on six benchmarks to evaluate NullSpaceRDAR: OTB100, VOT variations (VOT2018, VOT2019, and VOT2020), LaSOT, TrackingNet, UAV123, and GOT10k. The results show that NullSpaceRDAR outperforms the state-of-the-art trackers.