Robust infrared small target detection is of great essence for infrared search and track system. To detect the low signal-to-clutter ratio (SCR) target under the interference of high-intensity structural background, we propose an infrared small target detection method using multidirectional derivative and local contrast difference (MDLCD). Noting that infrared small target tends to have 2D Gaussian-like shape, we present a new multidirectional derivative model to reflect this distribution in each direction, which effectively enhances the target. Additionally, the adjacent background is applied to construct the local contrast difference model, whose role is to further suppress the high-intensity structural clutters. After this, the MDLCD map is obtained by weighting the above two filtered maps, along with an adaptive segmentation operation to finally extract the target. Experimental results verify that MDLCD achieves satisfactory performances in terms of SCR gain (SCRG) and background suppression factor (BSF).
RGB-T object tracking is a branch of visual tracking that has been widely applied to many fields, such as intelligent transportation and urban monitoring. Due to the interference of background clutter and occlusion, the existing trackers still suffer from the problems of unreasonable modal fusion strategy, insufficient feature extraction, and loss of semantic information. To solve these problems, we propose a residual learning-based two stream network for RGB-T object tracking. The overall feature extraction network is composed of three branches, and multi-layer convolutions are utilized to extract the features of visible, thermal, and fused images, respectively. First, aiming at improving the effectiveness of feature extraction, a weight generation module for hierarchical feature weight calculation is designed to guide the direction of feature fusion. Then, the residual block is employed to replace the single-layer convolution in order to increase the depth of the network, by which deeper semantic features are learned and the loss of semantic information is alleviated. Finally, a loss function with a penalty term is developed to adjust our network toward the direction of the best tracking performance of the dual modalities. This overcomes the negative impact of poor mode on model training. Experiments implemented on public RGB-T datasets indicate that our algorithm outperforms the recent state-of-the-art trackers in terms of both precision rate and success rate. Compared with the second-best comparison algorithm, our tracker improves the above two metrics by 0.4% and 1.5%, respectively. Our codes are available at https://github.com/MinjieWan/Residual-learning-based-two-stream-network-for-RGB-T-object-tracking.
Distinguishing the target from the background, judging target occlusion, and real-time processing are the problems that the visual tracking algorithm still needs to solve. Color information and position information of the target block are fused as new features to track the target under the framework of particle filtering. First, the hues, saturation, value space, and color integral graph of the image are constructed. The vector representation of the target is obtained on the color integral image by sparse matrix. Then, candidate particles are produced by a particle filter and the sampling mode of particles is adjusted by a uniform acceleration model. The difference of particles reflects the position and scale change of the target. Finally, the candidate with the smallest eigenvector projection error is taken as the tracking target and the feature template is updated based on the tracking results. The presented algorithm can be used to track a single target in the color image sequence and has some robustness to the scale change, occlusion, and morphological change of the target. Experiment results on public datasets show that the proposed algorithm performs favorably in both speed and tracking effect when compared with other conventional trackers.
Infrared (IR) small target detection in a single frame is a challenging task due to the lack of texture and color information and the interference of background clutters. In light of the two-dimensional Gaussian-like shape of IR small target, two properties from the perspective of local gradient and directional curvature (LGDC) are characterized. Specifically speaking, the local gradients in four quadrants as well as the curvatures from four directions should distribute in a regular way in the target region. Therefore, an LGDC map is computed from the input IR image so that the contrast between target and background can be greatly improved. By this means, we are able to extract the IR small target by a simple threshold related to the mean and standard deviation values of the LGDC map. Experiments implemented on real IR images verify that the proposed method can achieve satisfactory performance in terms of local contrast enhancement and background clutter suppression.
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