scholarly journals Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching

2016 ◽  
Keyword(s):  
Feature Matching ◽  
Vehicle Detection ◽  
Surveillance Video ◽  
Traffic Surveillance ◽  
Highway Traffic ◽  
Sift Feature ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking

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).

scholarly journals V-DaT: A Robust method for Vehicle Detection and Tracking

2021 ◽  
Vol 12 (2) ◽  
pp. 2492-2505
Author(s):  
Latha Anuj , Et. al.
Keyword(s):  
Background Subtraction ◽  
Vehicle Detection ◽  
Morphological Operations ◽  
Traffic Surveillance ◽  
Moving Vehicle ◽  
Directional Filtering ◽  
Detection And Tracking ◽  
Algorithmic Optimization ◽  
Vehicle Detection And Tracking ◽  
Moving Vehicle Detection

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

On-Road Vehicle Detection Based on Color Segmentation and Tracking Using Harris-SIFT

2012 ◽  
Vol 433-440 ◽  
pp. 5334-5338 ◽  
Author(s):  
Zhi Hui Zheng ◽  
Bo Wang
Keyword(s):  
Feature Matching ◽  
Vehicle Detection ◽  
Color Segmentation ◽  
Segmentation Method ◽  
Detection Scheme ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
On Line ◽  
Novel Method ◽  
Clustering Approach

This paper proposes a novel method for vehicle detection and tracking using a vehicle-mounted monocular camera in an intelligent vehicle system. Speed-based Adaptive Perception Zone (APZ) is first defined to ensure that the vehicle minimizes the spatial extent of the region it perceives according to its own speed. Vehicle candidates are generated using brake lights detection through color segmentation method and verified by a rule-based clustering approach. A tracking-by-detection scheme based on Harris-SIFT feature matching is then used to learn the template of the detected vehicle on line, localize and track the corresponding vehicle in live video. Our system was validated in real conditions in our prototype vehicle with state-of-the-art performance, equivalent and sometimes surpassing other methods recently published.

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