In distributed storage systems, storage nodes are organized in racks in which the cross-rack communication cost is much more expensive than the intra-rack communication cost. It is critical to design erasure codes that minimize the cross-rack update bandwidth which is defined as the total amount of symbols transferred across different racks during an update process. In this paper, we analyze the cross-rack update bandwidth of erasure codes for distributed storage systems. We derive a lower bound on the cross-rack update bandwidth and show that the proposed lower bound is achievable under certain parameters.

Visual tracking is a core component of intelligent transportation systems, especially for unmanned driving and road surveillance. Numerous convolutional neural network (CNN) trackers have achieved unprecedented performance. However, CNN features with regular spatial context relationships experience difficulty matching the rigid target templates when dramatic deformation and occlusion occur. In this paper, we propose a novel full Transformer Sub-patch Matching network for tracking (TSMtrack), which decomposes the tracked object into sub-patches, and interlaced matches the extracted sub-patches by leveraging the attention mechanism born with the Transformer. Roots in Transformer architecture, TSMtrack consists of image patch decomposition, sub-patch matching, and position prediction. Specifically, TSMtrack converts the whole frame into sub-patches and extracts the sub-patch features independently. By sub-patch matching and FFN-like prediction, TSMtrack enables independent similarity measurement between sub-patch features in an interlaced and iterative fashion. With a full Transformer pipeline implemented, we achieve a high-quality trade-off between tracking speed performance. Experiments on nine benchmarks demonstrate the effectiveness of our Transformer sub-patch matching framework. In particular, it realizes an AO of 75.6 on GOT-10K and SR of 57.9 on WebUAV-3M with 48 FPS on GPU RTX-2060s.

Traffic camera is one of the most ubiquitous traffic facilities, providing high coverage of complex, accident-prone road sections such as intersections. This work leverages traffic cameras to improve the perception and localization performance of autonomous vehicles at intersections. In particular, vehicles can expand their range of perception by matching the images captured by both the traffic cameras and on-vehicle cameras. Moreover, a traffic camera can match its images to an existing high-definition map (HD map) to derive centimeter-level location of the vehicles in its field of view. To this end, we propose AutoMatch - a novel system for real-time image registration, which is a key enabling technology for traffic camera-assisted perception and localization of autonomous vehicles. Our key idea is to leverage landmark keypoints of distinctive structures such as ground signs at intersections to facilitate image registration between traffic cameras and HD maps or vehicles. By leveraging the strong structural characteristics of ground signs, AutoMatch can extract very few but precise landmark keypoints for registration, which effectively reduces the communication/compute overhead. We implement AutoMatch on a testbed consisting of a self-built autonomous car, drones for surveying and mapping, and real traffic cameras. In addition, we collect two new multi-view traffic image datasets at intersections, which contain images from 220 real operational traffic cameras in 22 cities. Experimental results show that AutoMatch achieves pixel-level image registration accuracy within 88 milliseconds, and delivers an 11.7× improvement in accuracy, 1.4× speedup in compute time, and 17.1× data transmission saving over existing approaches.

Model object trackers largely rely on the online learning of a discriminative classifier from potentially diverse sample frames. However, noisy or insufficient amounts of samples can deteriorate the classifiers' performance and cause tracking drift. Furthermore, alterations such as occlusion and blurring can cause the target to be lost. In this paper, we make several improvements aimed at tackling uncertainty and improving robustness in object tracking. Our first and most important contribution is to propose a sampling method for the online learning of object trackers based on uncertainty adjustment: our method effectively selects representative sample frames to feed the discriminative branch of the tracker, while filtering out noise samples. Furthermore, to improve the robustness of the tracker to various challenging scenarios, we propose a novel data augmentation procedure, together with a specific improved backbone architecture. All our improvements fit together in one model, which we refer to as the Uncertainty Adjusted Tracker (UATracker), and can be trained in a joint and end-to-end fashion. Experiments on the LaSOT, UAV123, OTB100 and VOT2018 benchmarks demonstrate that our UATracker outperforms state-of-the-art real-time trackers by significant margins.

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with significant margins1