Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional Intersection over Union (IoU) distance measure. By leveraging projected probability distributions on the ground plane, our approach efficiently captures motion patterns and adeptly manages uncertainties introduced by homography projections. Remarkably, UCMCTrack, relying solely on motion cues, achieves state-of-the-art performance across a variety of challenging datasets, including MOT17, MOT20, DanceTrack and KITTI. More details and code are available at https://github.com/corfyi/UCMCTrack.

As human-machine interaction continues to evolve, the capacity for environmental perception is becoming increasingly crucial. Integrating the two most common types of sensory data, images, and point clouds, can enhance detection accuracy. Currently, there is no existing model capable of detecting an object's position in both point clouds and images while also determining their corresponding relationship. This information is invaluable for human-machine interactions, offering new possibilities for their enhancement. In light of this, this paper introduces an end-to-end Consistency Object Detection (COD) algorithm framework that requires only a single forward inference to simultaneously obtain an object's position in both point clouds and images and establish their correlation. Furthermore, to assess the accuracy of the object correlation between point clouds and images, this paper proposes a new evaluation metric, Consistency Precision (CP). To verify the effectiveness of the proposed framework, an extensive set of experiments has been conducted on the KITTI and DAIR-V2X datasets. The study also explored how the proposed consistency detection method performs on images when the calibration parameters between images and point clouds are disturbed, compared to existing post-processing methods. The experimental results demonstrate that the proposed method exhibits ex-cellent detection performance and robustness, achieving end-to-end consistency detection. The source code will be made publicly available at https://github.com/xifen523/COD.

Deep Belief Networks (DBNs) are state-of-the-art learning algorithms building on a subset of neural networks, Restricted Boltzmann Machine (RBM). DBNs are computationally intensive posing the question of whether DBNs can be FPGA accelerated. Fixed-point arithmetic can have an important influence on the execution time and prediction accuracy of a DBN. Previous studies have focused only on customized RBM accelerators with a fixed data-width. Our results experiments demonstrate that variable data-widths can obtain similar performance levels. We can also observe that the most suitable data-widths for different types of DBN are not unique or fixed. From this we conclude that a DBN accelerator should support various data-widths rather than only fixed one as done in previous work. The processing performance of DBN accelerators in FPGA is almost always constrained not by the capacity of the processing units, but by their on-chip RAM capacity and speed. We propose an efficient memory sub-system combining junction and padding methods to reduce bandwidth usage for DBN accelerators, which shows that supporting various data-widths is not as difficult as it may sound. The cost is only little in hardware terms and does not affect the critical path. We design a generation tool to help users reconfiguring the memory sub-system with arbitrary data-width flexibly. Our tool can also be used as an advanced IP core generator above FPGA memory controller supporting parallel memory access in irregular data-width for other applications.

A new MDS array erasure code, called DA-Code, which can tolerate double disk erasures for highly reliable data storage system is proposed in this paper. The DA-Code requires only XOR operations and achieves optimal encoding, updating and decoding complexity. The parity symbols are evenly distributed in the array, overcoming the bottleneck effects of repeated write operation. Detailed DA-Code's decoding algorithm for correcting double disk failures is provided. Analysis result shows that the new coding scheme has excellent performance. Thus, the DA-Code is practically very meaningful for storage systems which need high reliability.