Vehicle Tracking with Non-overlapping Views for Multi-camera Surveillance System

Jiang, Wenbin; Xiao, Chengwen; Jin, Hai; Zhu, Shuo; Lu, Zhiwei

doi:10.1109/hpcc.and.euc.2013.172

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…4. One may notice that while most research works split multi-camera object tracking into single-camera object tracking and inter-camera data fusion [33]- [37], we follow a slightly different approach. After the cameras are synchronised, a 2D object detector takes the camera videos as inputs, identifies the locations of vehicles in each frame, and classifies each detected object as car, truck, bus or motorbike.…”

Section: Vehicle Tracking Pipelinementioning

confidence: 99%

Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

Shan,

Penlington,

Gunner

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Accurate and reliable perception of the surrounding environment, e.g., detection and classification of nearby objects, is the primary and most important function of automated/autonomous vehicles. However, onboard perception systems face challenges in complex road segments due to various environmental effects, such as occlusions, or high sensor noise. A potential enhancement is to equip such environments with cost-effective infrastructures that perceive the environment and provide additional perception support to autonomous vehicles through vehicle-to-everything (V2X) communication technologies. This paper develops an experimental study of vehicle detection and tracking on a bird's eye view (BEV) map using raw video collected from several low-cost roadside monocular cameras with overlapping views installed near a motorway junction to support the merging of autonomous vehicles. The paper explains how to produce vehicle tracks from the camera infrastructure and reports the real-world evaluation of the proposed solution on a physical test bed in the UK's West Midland region.

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Section: Vehicle Tracking Pipelinementioning

confidence: 99%

Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

Shan,

Penlington,

Gunner

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…Nowadays, embedded video processing applications are widely spread in our daily life. Among these applications, we can mention public area video surveillance [1], crowd behaviour analysis for detecting abnormal activities [2,3], vehicle tracking [4], intelligent transportation systems [5,6], assisted living for elder people [7,8], simultaneous localization and mapping (SLAM) problem [9,10], counting passengers in vehicles [11], real-time autonomous localization [12], yawning detection [13], monitoring systems for kids safety, analyzing customer behaviour in markets, and so on.…”

Section: Introductionmentioning

confidence: 99%

ViPar: High-Level Design Space Exploration for Parallel Video Processing Architectures

Ali

Atitallah

Cadi

et al. 2019

International Journal of Reconfigurable Computing

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Embedded video applications are now involved in sophisticated transportation systems like autonomous vehicles and driver assistance systems. As silicon capacity increases, the design productivity gap grows up for the current available design tools. Hence, high-level synthesis (HLS) tools emerged in order to reduce that gap by shifting the design efforts to higher abstraction levels. In this paper, we present ViPar as a tool for exploring different video processing architectures at higher design level. First, we proposed a parametrizable parallel architectural model dedicated for video applications. Second, targeting this architectural model, we developed ViPar tool with two main features: (1) An empirical model was introduced to estimate the power consumption based on hardware utilization and operating frequency. In addition to that, we derived the equations for estimating the hardware utilization and execution time for each design point during the space exploration process. (2) By defining the main characteristics of the parallel video architecture like parallelism level, the number of input/output ports, the pixel distribution pattern, and so on, ViPar tool can automatically generate the dedicated architecture for hardware implementation. In the experimental validation, we used ViPar tool to generate automatically an efficient hardware implementation for a Multiwindow Sum of Absolute Difference stereo matching algorithm on Xilinx Zynq ZC706 board. We succeeded to increase the design productivity by converging rapidly to the appropriate designs that fit with our system constraints in terms of power consumption, hardware utilization, and frame execution time.

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A New Approach for Vehicle Recognition and Tracking in Multi-camera Traffic System

Jiang

Jin

et al. 2015

Algorithms and Architectures for Parallel Processing

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Vehicle Tracking with Non-overlapping Views for Multi-camera Surveillance System

Cited by 6 publications

References 14 publications

Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

ViPar: High-Level Design Space Exploration for Parallel Video Processing Architectures

A New Approach for Vehicle Recognition and Tracking in Multi-camera Traffic System

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