Multi-object tracking (MOT) is a significant and widespread research field in image processing and computer vision. The goal of the MOT task consists in predicting the complete tracklets of multiple objects in a video sequence. There are usually many challenges that degrade the performance of the algorithm in the tracking process, such as occlusion and similar objects. However, the existing MOT algorithms based on the tracking-by-detection paradigm struggle to accurately predict the location of the objects that they fail to track in complex scenes, leading to tracking performance decay, such as an increase in the number of ID switches and tracking drifts. To tackle those difficulties, in this study, we design a motion prediction strategy for predicting the location of occluded objects. Since the occluded objects may be legible in earlier frames, we utilize the speed and location of the objects in the past frames to predict the possible location of the occluded objects. In addition, to improve the tracking speed and further enhance the tracking robustness, we utilize efficient YOLOv4-tiny to produce the detections in the proposed algorithm. By using YOLOv4-tiny, the tracking speed of our proposed method improved significantly. The experimental results on two widely used public datasets show that our proposed approach has obvious advantages in tracking accuracy and speed compared with other comparison algorithms. Compared to the Deep SORT baseline, our proposed method has a significant improvement in tracking performance.

NAND flash storage devices are gaining popularity in consumer electronic devices, such as digital cameras, tablet PCs, and smartphones, due to their attractive features. However, write operations significantly limit the performance of the flash storage in consumer electronics devices due to their long latency and heavy overhead of garbage collection. Hence, NAND flash storage devices generally use RAM memory as a buffer to improve their write performance and to extend their lifetime. This paper proposes a novel buffer management algorithm (BMA), called as virtual-block-based buffer management scheme (VBBMS), which can make full use of both temporal and spatial localities at virtual-block level. The main difference with the existing BMAs is that the management granularity of VBBMS is a virtual block, while the management granularity of existing BMAs is a page or a block. In addition, VBBMS divides RAM into random request service region (RRSR), which is responsible for handling random requests, and sequential request service region (SRSR), which is responsible for handling sequential requests. When replacement happens, VBBMS selects victim virtual-block according to LRU policy in RRSR and first-in first-out (FIFO) policy in SRSR. VBBMS has been extensively evaluated under various realistic workloads, and our benchmark results show that VBBMS can improve buffer hit ratio, reduce response time, and erase times, compared to five existing BMAs.

In this paper, an improved multi-exposure image fusion method for intelligent transportation systems (ITS) is proposed. Further, a new multi-exposure image dataset for traffic signs, TrafficSign, is presented to verify the method. In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results have adaptive characteristics similar to that of the source image. Considering the weather factor and environmental noise, the source image is preprocessed by bilateral filtering and dehazing algorithm. Further, this paper uses adaptive optimization to improve the quality of the output image of the fusion model. The qualitative and quantitative experiments on the new dataset show that the multi-exposure image fusion algorithm proposed in this paper is effective and practical in the ITS.

Multi-vehicle tracking is one of the most crucial components of an intelligent transportation system (ITS). However, when it comes to busy traffic flow, tracking targets robustly becomes more problematic due to occlusion, motion blur, high appearance similarity, etc. To achieve accurate and efficient tracking performance, we present a novel multi-vehicle tracking method based on the enhanced detection model and joint scoring strategy. Specifically, the former aims to (1) adopt lightweight yet efficient YOLOv5s to improve detection accuracy and running speed, and (2) incorporate the CBAM and transformer encoder modules into the detection model to generate the refined features for the target localization. The Latter preferentially provides high-confidence detections and tracklets for subsequent data association, significantly reducing the number of identity switches and redundant vehicle trajectories caused by mutual occlusion, similar object interference, etc. We evaluated the proposed multivehicle tracking approach on the UA-DETRAC vehicle tracking dataset and demonstrated its superior capabilities through intensive comparison and analysis. Moreover, our proposed method runs at 24.4 FPS on a single GPU and meets the real-time requirement.