Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.

Siamese visual tracking, viewed as a problem of max-similarity matching to the target template, has absorbed increasing attention in computer vision. However, it is a challenge for current Siamese trackers that the demands of balance between accuracy in real-time tracking and robustness in long-time tracking are hard to meet. This work proposes a new Siamese based tracker with a dual-pipeline correlated fusion network (named as ADF-SiamRPN), which consists of one initial template for robust correlation, and the other transient template with the ability of adaptive feature optimal selection for accurate correlation. By the promotion from the learnable correlation-response fusion network afterwards, we are in pursuit of the synthetical improvement of tracking performance. To compare the performance of ADF-SiamRPN with state-of-the-art trackers, we conduct lots of experiments on benchmarks like OTB100, UAV123, VOT2016, VOT2018, GOT-10k, LaSOT and TrackingNet. The experimental results of tracking demonstrate that ADF-SiamRPN outperforms all the compared trackers and achieves the best balance between accuracy and robustness.