A new approach to multiple vehicle tracking in intersections using harris corners and adaptive background subtraction

Bragatto, T.A.C.; Ruas, G.S.I.; Benso, Victor Alberto; Lamar, M.V.; Aldigueri, D.; Teixeira, G.L.; Yamashita, Yuichi

doi:10.1109/ivs.2008.4621293

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…Intelligent visual surveillance for road vehicles generally use vehicle tracking to get the trajectory. The vehicle-tracking algorithms can be classified into four major categories: region-based tracking algorithms [1], active contour-based tracking algorithms [2], feature-based tracking algorithms [3], and model-based tracking algorithms [4]. The primary algorithms along with their strengths and weaknesses for vehicle-tracking algorithms are summarized in table 1.…”

Section: Trajectory Acquisition and Preprocessingmentioning

confidence: 99%

Automatic Traffic Abnormality Detection in Traffic Scenes: An Overview

Liu¹,

Nie²,

Jiang³

et al. 2017

dtetr

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In recent years, with the continuous improvement of the computer's ability of data processing, and the rapid development of image processing and pattern recognition technology, the traffic information automatic detection technology based on video has become a research focus in the intelligent transportation field. This article presents an overview of the state-of-theart methods of automatic traffic abnormality detection and their extensions. In this paper, we introduced automatic traffic abnormality detection methods based on trajectory analysis, based on optical flow, based on image visual features descriptor and contrasted their advantages and disadvantages.

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Section: Trajectory Acquisition and Preprocessingmentioning

confidence: 99%

Automatic Traffic Abnormality Detection in Traffic Scenes: An Overview

Liu¹,

Nie²,

Jiang³

et al. 2017

dtetr

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show abstract

“…Once the light turns green, the cars move again and new tracking of them is constructed. In summary, it has a bad effect on the continuous tracking of foreground objects and reduces the accuracy of some high-level understanding methods, such as [3, 4]. Unlike previous methods, this paper focuses on how to adjust learning rate according to the real-time and accurate physical world signals from other sensors.…”

Section: Introductionmentioning

confidence: 99%

Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks

Ding

Wang

Liu

2014

International Journal of Distributed Sensor Networks

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Foreground detection plays an important role in the traffic surveillance applications, especially in urban intersections. Background subtraction is an efficient approach to segment the background and foreground with static cameras from video sensor networks. But when modelling the background, most statistical techniques adjust the learning rate only based on the changes from video sequences, which is a crucial parameter controlling the updating speed. This causes a slow adaptation to sudden environmental changes. For example, a stopped car fuses into background before moving again, and it lowers the segmentation performance. This paper proposes an efficient way to address the problem by accounting for the physical world signal in traffic junctions. It assigns an adaptive learning rate to each pixel by integrating traffic light signal obtained from sensor networks.Combined with abundant physical world signals, background subtraction method is able to adapt itself to the outside world changes instantly. We test our approach in real urban traffic intersection; experimental results show that the new method increases the accuracy of detection and has a promising future.

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“…In addition, to robustly detect vehicles, noises from cluttered environment, such as traffics i g n s , shadows and buildings, should be prevented from detection. Therefore in the literature, some researches focus on vehicle detection based on shadow features [6], corner detection [7,8], edge based [9,10], and Haar-like features [11,12]. In addition, Sun et alu s e dG a b o rfilters to extract different textures and then verified each candidate of vehicles using a SVM classifier [13].…”

Section: Introductionmentioning

confidence: 99%

Real‐time dynamic vehicle detection on resource‐limited mobile platform

Chen¹,

Chen

Wang³

2013

IET Computer Vision

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Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of cameras and optical devices has made it economically feasible to deploy front‐mounted intelligent systems for visual‐based event detection. Prior to vehicle event detection, detecting vehicles robustly in real time is challenging, especially conducting detection process in images captured by a dynamic camera. Therefore in this study, a robust vehicle detector is developed. The proposed contribution is three‐fold. Road modelling is first proposed to confine detection area for maintaining low computation complexity and reducing false alarms as well. Haar‐like features and eigencolours are then employed for the vehicle detector. To tackle the occlusion problem, chamfer distance is used to estimate the probability of each individual vehicle. Consequently, to find the bounding box of vehicle candidate, the authors take the configuration of a normalised chamfer distance map that corresponds to the maximum score as the target using exponential entropy. Experiments on an extensive dataset show that the proposed system can effectively detect vehicles under different lighting and traffic conditions, and thus demonstrates its feasibility in real‐world environments.

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A new approach to multiple vehicle tracking in intersections using harris corners and adaptive background subtraction

Cited by 9 publications

References 17 publications

Automatic Traffic Abnormality Detection in Traffic Scenes: An Overview

Automatic Traffic Abnormality Detection in Traffic Scenes: An Overview

Using Traffic Light Signal to Enhance Intersection Foreground Detection Based on Video Sensor Networks

Real‐time dynamic vehicle detection on resource‐limited mobile platform

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