Design and Implementation of Multiple-Vehicle Detection and Tracking Systems with Machine Learning

2014 ◽  
Author(s):  
Shen-Fu Hsiao ◽  
Guan-Fu Yeh ◽  
Je-Chi Chen
Keyword(s):  
Machine Learning ◽  
Vehicle Detection ◽  
Tracking Systems ◽  
Design And Implementation ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking

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 pose estimation for vehicle detection and tracking based on road direction

2017 ◽  
Vol 3 (1) ◽  
pp. 35
Author(s):  
Adhi Prahara ◽  
Ahmad Azhari ◽  
Murinto Murinto
Keyword(s):  
Vehicle Detection ◽  
Training Data ◽  
Support Vector ◽  
Traffic Surveillance ◽  
Vehicle Performance ◽  
Surveillance Camera ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Different Color ◽  
Linear Svm

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).

Microscopic Traffic Data Collection by Remote Sensing

2003 ◽  
Vol 1855 (1) ◽  
pp. 121-128 ◽  
Author(s):  
S. P. Hoogendoorn ◽  
H. J. Van Zuylen ◽  
M. Schreuder ◽  
B. Gorte ◽  
G. Vosselman
Keyword(s):  
Data Collection ◽  
Digital Camera ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Lateral Position ◽  
Aerial Images ◽  
Detection And Tracking ◽  
Vehicle Trajectories ◽  
Vehicle Detection And Tracking ◽  
Traffic Data Collection

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

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