Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images. To address this issue, we propose a multi-level similarity model under a Siamese framework for robust TIR object tracking. Specifically, we compute different pattern similarities using the proposed multi-level similarity network. One of them focuses on the global semantic similarity and the other computes the local structural similarity of the TIR object. These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors. In addition, we design a simple while effective relative entropy based ensemble subnetwork to integrate the semantic and structural similarities. This subnetwork can adaptive learn the weights of the semantic and structural similarities at the training stage. To further enhance the discriminative capacity of the tracker, we propose a large-scale TIR video sequence dataset for training the proposed model. To the best of our knowledge, this is the first and the largest TIR object tracking training dataset to date. The proposed TIR dataset not only benefits the training for TIR object tracking but also can be applied to numerous TIR visual tasks. Extensive experimental results on three benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Thermal infrared (TIR) pedestrian tracking is one of the important components among the numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribute based evaluation. In addition to the dataset, we carried out the large-scale evaluation experiments on our benchmark dataset using nine publicly available trackers. The experimental results help us understand the strengths and weaknesses of these trackers. In addition, in order to gain more insight into the TIR pedestrian tracker, we divided its functions into three components: feature extractor, motion model, and observation model. Then, we conducted three comparison experiments on our benchmark dataset to validate how each component affects the tracker's performance. The findings of these experiments provide some guidelines for future research.

Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model requires numerous labeled training samples, and the number and quality of these samples directly affect the representational capability of the trained model. However, this approach is restrictive in practice, because manually labeling such a large number of training samples is time-consuming and prohibitively expensive. In this article, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNN model. Under the guidance of active learning, the tracker based on the trained deep CNN model can achieve competitive tracking performance while reducing the labeling cost. More specifically, to ensure the diversity of selected samples, we propose an active learning method based on multiframe collaboration to select those training samples that should be and need to be annotated. Meanwhile, considering the representativeness of these selected samples, we adopt a nearest-neighbor discrimination method based on the average nearest-neighbor distance to screen isolated samples and low-quality samples. Therefore, the training samples’ subset selected based on our method requires only a given budget to maintain the diversity and representativeness of the entire sample set. Furthermore, we adopt a Tversky loss to improve the bounding box estimation of our tracker, which can ensure that the tracker achieves more accurate target states. Extensive experimental results confirm that our active-learning-based tracker (ALT) achieves competitive tracking accuracy and speed compared with state-of-the-art trackers on the seven most challenging evaluation benchmarks. Project website: https://sites.google.com/view/altrack/.