A hierarchical clustering of features approach for vehicle tracking in traffic environments

2016 ◽  
Vol 9 (4) ◽  
pp. 354-368 ◽  
Author(s):  
Anan Banharnsakun ◽  
Supannee Tanathong
Keyword(s):  
Hierarchical Clustering ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Corner Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Night Time ◽  
Content Type ◽  
Traffic Conditions ◽  
Detection And Tracking

Purpose Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring system, vehicle detection is an essential and challenging task. In the previous studies, many vehicle detection methods have been presented. These proposed approaches mostly used either motion information or characteristic information to detect vehicles. Although these methods are effective in detecting vehicles, their detection accuracy still needs to be improved. Moreover, the headlights and windshields, which are used as the vehicle features for detection in these methods, are easily obscured in some traffic conditions. The paper aims to discuss these issues. Design/methodology/approach First, each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model. Next, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the moving objects frame by frame. Findings Using the proposed method, it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement (waving trees), which has to be deemed as background. In addition, the proposed method is able to deal with different vehicle shapes such as cars, vans, and motorcycles. Originality/value This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

scholarly journals Optimized Multi-layer Machine Learning Technique for Movement Detection and Tracking in Video Data Stream

2019 ◽  
Vol 8 (10) ◽  
pp. 1151-1159
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Error Rates ◽  
Moving Object Detection ◽  
Video Data ◽  
Moving Object ◽  
Detection Accuracy ◽  
Efficient System ◽  
Detection And Tracking ◽  
The Given

With the advent in technology, security and authentication has become the main aspect in computer vision approach. Moving object detection is an efficient system with the goal of preserving the perceptible and principal source in a group. Surveillance is one of the most crucial requirements and carried out to monitor various kinds of activities. The detection and tracking of moving objects are the fundamental concept that comes under the surveillance systems. Moving object recognition is challenging approach in the field of digital image processing. Moving object detection relies on few of the applications which are Human Machine Interaction (HMI), Safety and video Surveillance, Augmented Realism, Transportation Monitoring on Roads, Medical Imaging etc. The main goal of this research is the detection and tracking moving object. In proposed approach, based on the pre-processing method in which there is extraction of the frames with reduction of dimension. It applies the morphological methods to clean the foreground image in the moving objects and texture based feature extract using component analysis method. After that, design a novel method which is optimized multilayer perceptron neural network. It used the optimized layers based on the Pbest and Gbest particle position in the objects. It finds the fitness values which is binary values (x_update, y_update) of swarm or object positions. Method and output achieved final frame creation of the moving objects in the video using BLOB ANALYSER In this research , an application is designed using MATLAB VERSION 2016a In activation function to re-filter the given input and final output calculated with the help of pre-defined sigmoid. In proposed methods to find the clear detection and tracking in the given dataset MOT, FOOTBALL, INDOOR and OUTDOOR datasets. To improve the detection accuracy rate, recall rate and reduce the error rates, False Positive and Negative rate and compare with the various classifiers such as KNN, MLPNN and J48 decision Tree.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Detection and tracking of moving object using modified background subtraction and Kalman filter

2021 ◽  
Vol 11 (1) ◽  
pp. 217
Author(s):  
Jeevith S. H. ◽  
Lakshmikanth S.
Keyword(s):  
Kalman Filter ◽  
Background Subtraction ◽  
Moving Objects ◽  
Adaptive Threshold ◽  
Vital Role ◽  
Moving Object ◽  
Navigation Systems ◽  
Subtraction Technique ◽  
Night Time ◽  
Detection And Tracking

Moving object detection and tracking (MODT) is the major challenging issue in computer vision, which plays a vital role in many applications like robotics, surveillance, navigation systems, militaries, environmental monitoring etc. There are several existing techniques, which has been used to detect and track the moving object in Surveillance system. Therefore it is necessary to develop new algorithm or modified algorithm which is robust to work in both day and night time. In this paper, modified BGS technique is proposed. The video is first converted to number of frames, then these frame are applied to modified background subtraction technique with adaptive threshold which gives detected object. Kalman filter technique is used for tracking the detected object. The experimental results shows this proposed method can efficiently and correctly detect and track the moving objects with less processing time which is compared with existing techniques.

Automatic vehicle detection and counting approach using low-rank representation and locality-constrained linear coding

2018 ◽  
Vol 35 (8) ◽  
pp. 2825-2843
Author(s):  
Qizi Huangpeng ◽  
Wenwei Huang ◽  
Hanyi Shi ◽  
Jun Fan
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Intelligent Transportation System ◽  
Vehicle Detection ◽  
Low Rank ◽  
Step Method ◽  
Lighting Condition ◽  
Content Type ◽  
Public Data ◽  
Low Rank Representation

Purpose Vehicles estimation can be used in evaluating traffic conditions and facilitating traffic control, which is an important task in intelligent transportation system. The paper aims to propose a vehicle-counting method based on the analysis of surveillance videos. Design/methodology/approach The paper proposes a novel two-step method using low-rank representation (LRR) detection and locality-constrained linear coding (LLC) classification to count the number of vehicles in traffic video sequences automatically. The proposed method is based on an offline training to understand an LLC-based classifier with extracted features for vehicle and pedestrian classification, followed by an online counting algorithm to count the number of vehicles detected from the image sequence. Findings The proposed method allows delivery estimation (counting the number of vehicles at each frame only) and total number estimation of vehicles shown in the scene. The paper compares the proposed method with other similar methods on three public data sets. The experimental results show that the proposed method is competitive and effective in terms of computational speed and evaluation accuracy. Research limitations/implications The proposed method does not consider illumination. Hence, the results might be unsatisfactory under low-lighting condition. Therefore, researchers are encouraged to add a term that controls the illumination changes into the energy function of vehicle detection in future work. Originality/value The paper bridges the gap between LRR detection and vehicle counting by taking advantage of existing LLC classification algorithm to distinguish different moving objects.

scholarly journals A Real-Time Vehicle Detection System under Various Bad Weather Conditions Based on a Deep Learning Model without Retraining

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5731 ◽  
Author(s):  
Xiu-Zhi Chen ◽  
Chieh-Min Chang ◽  
Chao-Wei Yu ◽  
Yen-Lin Chen
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Learning Model ◽  
Detection Methods ◽  
Computational Time ◽  
Detection Accuracy ◽  
Bad Weather ◽  
Deep Learning Model

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder–decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.

Moving Vehicle Detection of Airborne Video on SURF

2013 ◽  
Vol 734-737 ◽  
pp. 2815-2818
Author(s):  
Hui Liu ◽  
Chun Xian Gao ◽  
Xing Hao Ding ◽  
Zhe Zeng
Keyword(s):  
Detection Efficiency ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Normal Vector ◽  
Detection Accuracy ◽  
External Interference ◽  
Moving Vehicle ◽  
Low Level ◽  
Wide Range ◽  
Moving Vehicle Detection

Due to the high mobility, a wide range of monitoring, air mobile platform-based vehicle detection and tracking system is becoming core of the investigation and the monitoring. Self-motion of the camera and external interference caused by the low-level platform led to instability of the obtained video and affect the correct detection of moving targets and subsequent analysis. For the characteristics for low-level video, an image stabilization algorithm based on SURF combined with normal vector of optical flow is proposed to solve moving vehicle detection low-altitude video. From the experimental results can be seen: (1) compared to other moving vehicle detection methods, the method proposed can get better detection efficiency and detection accuracy; (2) in the complex context, this method can effectively detect moving vehicles. The experiments show that this method has some theoretical and application value of space-based video moving target detection.

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 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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