Grayscale-Thermal Tracking via Inverse Sparse Representation based Collaborative Encoding

Anchor free based Siamese network tracker with transformer for RGB-T tracking

In recent years, many RGB-THERMAL tracking methods have been proposed to meet the needs of single object tracking under different conditions. However, these trackers are based on ANCHOR-BASED algorithms and feature cross-correlation operations, making it difficult to improve the success rate of target tracking. We propose a siamAFTS tracking network, which is based on ANCHOR-FREE and utilizes a fully convolutional training network with a Transformer module, suitable for RGB-THERMAL target tracking. This model addresses the issue of low success rate in current mainstream algorithms. We also incorporate channel and channel spatial attention modules into the network to reduce background interference on predicted bounding boxes. Unlike current ANCHOR-BASED trackers such as MANET, DAPNet, SGT, and ADNet, the proposed framework eliminates the use of anchor points, avoiding the challenges of anchor hyperparameter tuning and reducing human intervention. Through repeated experiments on three datasets, we ultimately demonstrate the improved success rate of target tracking achieved by our proposed tracking network.

Discriminatively Unsupervised Learning Person Re-Identification via Considering Complicated Images

State-of-the-art purely unsupervised learning person re-ID methods first cluster all the images into multiple clusters and assign each clustered image a pseudo label based on the cluster result. Then, they construct a memory dictionary that stores all the clustered images, and subsequently train the feature extraction network based on this dictionary. All these methods directly discard the unclustered outliers in the clustering process and train the network only based on the clustered images. The unclustered outliers are complicated images containing different clothes and poses, with low resolution, severe occlusion, and so on, which are common in real-world applications. Therefore, models trained only on clustered images will be less robust and unable to handle complicated images. We construct a memory dictionary that considers complicated images consisting of both clustered and unclustered images, and design a corresponding contrastive loss by considering both kinds of images. The experimental results show that our memory dictionary that considers complicated images and contrastive loss can improve the person re-ID performance, which demonstrates the effectiveness of considering unclustered complicated images in unsupervised person re-ID.

Multi-Modal Sparse Tracking by Jointing Timing and Modal Consistency

In this paper, we propose a multi-modal sparse tracking by jointing timing and modal consistency to locate the target location with the similarity of multiple local appearances. First, we propose an alignable patching strategy for red-green-blue (RGB) color mode and thermal infrared mode to adapt to the local changes of the target. Second, we propose a consistency expression of the corresponding aligned patches between the modes and the correlation of the gaussian mapping within mode to reconstruct the target judgment likelihood function. Finally, we propose an updating scenario based on timing correlation and mode sparsity to fit with the target changes. According to the experimental results, significant improvement in terms of tracking accuracy can be achieved on average compared with the state-of-the-art algorithms. The source code of our algorithm is available on https://github.com/Liincq/tracker.