Affinity Propagation Feature Clustering with Application to Vehicle Detection and Tracking in Road Traffic Surveillance

2010 ◽  
Author(s):  
Jun Yang ◽  
Yang Wang ◽  
Arcot Sowmya ◽  
Bang Zhang ◽  
Jie Xu ◽  
...  
Keyword(s):  
Road Traffic ◽  
Vehicle Detection ◽  
Affinity Propagation ◽  
Traffic Surveillance ◽  
Feature Clustering ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Propagation Feature

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.

scholarly journals Various Methods of Moving Object Detection Techniques in Vehicular Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1622-1624
Keyword(s):  
Background Subtraction ◽  
Autonomous Vehicles ◽  
Active Contour ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Future Directions ◽  
Detection Techniques ◽  
Traffic Conditions ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking

With the invention of autonomous vehicles, it make easier to know about the future directions of the vehicles. The main purposes of vehicle detection and tracking are to identify the on-road traffic conditions, hazards or hurts as well as to communicate with other on-road vehicles/objects. To meet the above requirements, the following methodologies are useful which are like Fuzzy based, Radar and V2V fusion, Background subtraction, Active contour, Single learning based method and etc., Those methodologies are implementing either independently or collaboratively may lead to focus towards not only on the vehicles and other objects of our interest. In order to track the vehicles, first to detect the various objects and from them only it is possible to identify the vehicle objects. After the identification of vehicular objects, we can segregate them according to their sub-category. In this paper, various object/vehicle detection techniques have been discussed. The process of segmentation and separation of vehicle objects and its types can be possible by implementing a certain fuzzy rules. Detection of an object using various fusion techniques makes more effective in terms of obtaining the information regarding that particular vehicle directly or via nearby vehicles. While capturing the object, there is a possibility of false detection. This can be avoided by clearly eliminating the shadows, illumination and etc., from capturing object image. This kind of elimination process is termed as background subtraction. Active contour is one another detection method which gives us succession of images from which the internal and external borders of several objects can be identified. By a single extraction various objects are identified and then given to different detectors according to their sub-category. All these kind of techniques are discussed in this paper.

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