When used for object tracking, the discriminative correlation filter (DCF) is effective, but its performance is often burdened by undesirable boundary effects. Meanwhile, when there is too much background information in training samples of the DCF, it will be easier to learn the area deviating from the tracking object. Further, factors such as illumination variation, partial/full occlusion of the object, and variations in the object appearance, render the response map aberrance of the correlation filter (CF) more prone to occur. To overcome the aforementioned problems, an object tracking model based on a time-varying Spatio-temporal regularized correlation filter with aberrance repression is proposed in this paper. Firstly, by adding a regularized term to the objective function of the traditional CFs to limit the change rate of the response map generated in the object detection phase, the proposed tracker can obviously repress the aberrance of the response maps; secondly, by adjusting the filter to the object regions suitable for tracking with high confidence scores with a time-varying spatial reliability map, the proposed tracker effectively overcomes the adverse effects caused by the boundary effect; and finally, by introducing a temporal regularized term, the proposed tracker also has superior tracking ability for the partial occluded objects and those with large appearance variations. Significant experiments on the OTB100, VOT2016, TC128, and UAV 123 datasets have revealed that although the proposed tracker only uses the histogram of directional gradient (HOG) and color name (CN) features, the performance thereof outperformed many state-of-the-art trackers based on DCF and deep-based frameworks in terms of tracking accuracy, tracking success rate, and A-R rank, etc.

Visual navigation involves a movable robotic agent striving to reach a point goal (target location) using vision sensory input. While navigation with ideal visibility has seen plenty of success, it becomes challenging in suboptimal visual conditions like poor illumination, where traditional approaches suffer from severe performance degradation. We propose E3VN (echo-enhanced embodied visual navigation) to effectively perceive the surroundings even under poor visibility to mitigate this problem. This is made possible by adopting an echoer that actively perceives the environment via auditory signals. E3VN models the robot agent as playing a cooperative Markov game with that echoer. The action policies of robot and echoer are jointly optimized to maximize the reward in a two-stream actor-critic architecture. During optimization, the reward is also adaptively decomposed into the robot and echoer parts. Our experiments and ablation studies show that E3VN is consistently effective and robust in point goal navigation tasks, especially under nonideal visibility.

RGBT tracking has been increasingly investigated in visual tracking due to the strong complementary nature of visible and infrared images. However, in the established RGBT tracking algorithms, multiscale information has not been well exploited and utilized, which limits the performance of the tracker. In this paper, a novel multibranch adaptive fusion network is proposed, which aggregates multiscale information from multiple branches. Specifically, our backbone network draws on the modified VGG-M. To extract the multiscale features, we design a multiscale adapter, which adds two small convolution kernel branches to the backbone in each layer and each modality in a parallel manner. We also design a multibranch fusion module to adaptively aggregate the features from multiple branches and the previous layer. Moreover, we propose a multimodal fusion module for aggregating features between modalities, which could mitigate the impact of noise from low-quality sources. Finally, many results on two recent RGBT tracking datasets show that our method significantly outperforms other state-of-the-art tracking methods.

The scattering and absorption of light results in the degradation of image in sandstorm scenes, it is vulnerable to issues such as color casting, low contrast and lost details, resulting in poor visual quality. In such circumstances, traditional image restoration methods cannot fully restore images owing to the persistence of color casting problems and the poor estimation of scene transmission maps and atmospheric light. To effectively correct color casting and enhance visibility for such sand dust images, we proposed a sand dust image enhancement algorithm using the red and blue channels, which consists of two modules: the red channel-based correction function (RCC) and blue channel-based dust particle removal (BDPR), the RCC module is used to correct color casting errors, and the BDPR module removes sand dust particles. After the dust image is processed by these two modules, a clear and visible image can be produced. The experimental results were analyzed qualitatively and quantitatively, and the results show that this method can significantly improve the image quality under sandstorm weather and outperform the state-of-the-art restoration algorithms.