The application of machine vision techniques in Vehicle-to-Everything (V2X) scenarios within Intelligent Connected Systems (ICS) has gained increasing importance with advancements in 6G communication technology. However, the stringent latency and bandwidth requirements of most machine vision applications pose significant challenges to the existing infrastructure. Hence, there is a dearth of prior research examining whether the latency of real applications in ICS aligns with the needs of machine vision scenarios, let alone any performance evaluations conducted in this regard. In this paper, we conduct a comprehensive literature review and proposed a novel machine vision architecture that can analyze traffic data in real-time in the V2X scenario within ICS. Furthermore, based on the end-to-end latency assessment of the system, we outline a plan to optimize the latency as per the requirements of the machine vision application. Our findings show that with appropriate algorithms and architecture, the ICS system can meet the stringent needs of machine vision applications. Our research can provide valuable insights as a guideline on ICS with high latency requirements and therefore pave the way for future explorations in this field. 

Vehicle-Based Machine Vision Approaches in Intelligent Connected System

This paper introduces an invariant Hough random ferns (IHRF) incorporating rotation and scale invariance into the local feature description, random ferns classifier training, and Hough voting stages. It is especially suited for object detection under changes in object appearance and scale, partial occlusions, and pose variations. The efficacy of this approach is validated through experiments on a large set of challenging benchmark datasets, and the results demonstrate that the proposed method outperforms state-of-the-art conventional methods such as bounding-box-based and part-based methods. Additionally, we also propose an efficient clustering scheme based on the local patches’ appearance and their geometric relations that can provide pixel-accurate, top-down segmentations from IHRF back-projections. This refined segmentation can be used to improve the quality of online object tracking because it avoids the drifting problem. Thus, an online tracking framework based on IHRF, which is trained and updated in each frame to distinguish and segment the object from the background, is established. Finally, the experimental results on both object segmentation and long-term object tracking show that this method yields accurate and robust tracking performance in a variety of complex scenarios, especially in cases of severe occlusions and nonrigid deformations.

/pdf/invariant-hough-random-ferns-for-object-detection-and-1pij12954u.pdf

Invariant Hough Random Ferns for Object Detection and Tracking

Visual multi-object tracking is an important task within the field of computer vision. The goal of this paper is to track a variable number of unknown objects in complex scenes automatically using a moving and un-calibrated camera and it devotes to overcome the challenging problems including illumination and scale variations, viewpoint variations and significant occlusions, etc. In this paper, a binary representation containing color and gradient information is utilized to obtain unique features so that the objects can be easily distinguished from each other in the feature space. In addition, an online learning framework based on a cascaded classifier which is trained and updated in each frame to distinguish the object from the background is proposed for long-term tracking. The experimental results on both quantitative evaluations and multi-object tracking show that this approach yields an accurate and robust tracking performance in a large variety of complex scenarios.

http://ieeexplore.ieee.org/iel7/6731716/6743112/06743213.pdf

Online learning of cascaded classifier designed for multi-object tracking

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Online learning of cascaded classifier designed for multi-object tracking