scholarly journals Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qianxia Cao ◽  
Zhengwu Wang ◽  
Kejun Long
Keyword(s):  
Sparse Representation ◽  
Heavy Traffic ◽  
Urban Traffic ◽  
Detection Methods ◽  
Video Sequences ◽  
Foreground Detection ◽  
Qualitative And Quantitative ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Slow Moving

In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i -LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations.

scholarly journals Nighttime Vehicle Detection and Tracking with Occlusion Handling by Pairing Headlights and Taillights

2020 ◽  
Vol 10 (11) ◽  
pp. 3986
Author(s):  
Tuan-Anh Pham ◽  
Myungsik Yoo
Keyword(s):  
Machine Learning ◽  
Vehicle Detection ◽  
Vehicle Tracking ◽  
Detection Methods ◽  
Occlusion Handling ◽  
Tracking Method ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Vehicle Position

In recent years, vision-based vehicle detection has received considerable attention in the literature. Depending on the ambient illuminance, vehicle detection methods are classified as daytime and nighttime detection methods. In this paper, we propose a nighttime vehicle detection and tracking method with occlusion handling based on vehicle lights. First, bright blobs that may be vehicle lights are segmented in the captured image. Then, a machine learning-based method is proposed to classify whether the bright blobs are headlights, taillights, or other illuminant objects. Subsequently, the detected vehicle lights are tracked to further facilitate the determination of the vehicle position. As one vehicle is indicated by one or two light pairs, a light pairing process using spatiotemporal features is applied to pair vehicle lights. Finally, vehicle tracking with occlusion handling is applied to refine incorrect detections under various traffic situations. Experiments on two-lane and four-lane urban roads are conducted, and a quantitative evaluation of the results shows the effectiveness of the proposed method.

scholarly journals A New Hybrid Proposed Algorithm for Multiple Vehicle Detection and Tracking in a Day-Time Environment

2019 ◽  
Vol 8 (2) ◽  
pp. 457-469
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Data Sets ◽  
Front View ◽  
Real Time Traffic ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Training Samples

Multiple Vehicle detection and tracking is one of the hot research topics in the field of intelligent transportation systems, image processing, computer vision, robotics whereas applications are real time traffic monitoring, lane estimation, accident avoidance, alarm signal to indicate road accidents to save the public safety and so on. There exists a numerous higher level applications are motivated by a young researchers and scientists to identify the newly advanced techniques in which to solve the real time traffic problems using machine learning and deep learning methods to track multiple vehicles accurately. To addresses the various existing challenges in machine learning and deep learning based multiple vehicle detection and tracking algorithms namely camera oscillation, shadowing, changing in background motion, cluttering, camouflage etc. for the detection rate decreases dramatically when the distribution of the training samples and the scene target samples do not match. To address this issue, a new hybrid model of two-tier classifier of Haar+HOG, SVM+AdaBoost classifier algorithm based on a feature extraction algorithm is proposed in this paper. Inspired by the Adaptive Discrete Classifiers mechanism multiple relatively independent source samples are first used to build multiple classifiers and then particle grouping is used to generate the target training samples with confident scores. The global manual feature extraction ability of deep convolutional neural network is then used to perform source-target scene feature similarity calculation with a deep auto encoder in order to design a composite deep structure based adaptive discrete classifier and its global training method. The main contributions of this paper are threefold: 1) To improve the overall accuracy rate of multiple vehicle detection and tracking of front-view vehicles alone rather than full-sided vehicles. 2) The novelty of our proposed work is for particle grouping of multi-vehicles such as car, bus and lorry. 3) To propose the tracking of front- view multi- vehicles in linear and non-linear motion using particle and extended kalman filter along with hybrid new multi-vehicle tracking algorithm and attains 93.6% of accuracy is shown in the experimental results. We evaluates our proposed method with standard data sets PETS 2016 and 5 self-data sets iROAD were manually collected on traffic road and compared with the existing state of the art approaches and along with the Experiments on the Kitti dataset and a 3 different self -data set captured by our group demonstrate that the proposed method performs better than the existing machine-learning based vehicle detection methods. In addition, compared with the existing automatic feature extraction and region based object detection methods, our new hybrid method improves the overall detection rate by an average of approximately 5% of existing methods.

scholarly journals Vehicle detection and tracking for traffic management

2021 ◽  
Vol 10 (1) ◽  
pp. 66
Author(s):  
Mallikarjun Anandhalli ◽  
Vishwanth P. Baligar ◽  
Pavana Baligar ◽  
Pooja Deepsir ◽  
Mithali Iti
Keyword(s):  
Traffic Management ◽  
Vehicle Detection ◽  
Research Area ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Corner Points ◽  
Computer Research

<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>

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