33,979 research outputs found
Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data
The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis
Moving Vehicle Identification using Background Registration Technique for Traffic Surveillance
Real-time segmentation of moving regions in image
sequences is a fundamental step in many vision systems
including automated visual surveillance and human-machine
interface. In this paper we present a framework for detecting
some important but unknown knowledge like vehicle
identification and traffic flow count. The objective is to
monitor activities at traffic intersections for detecting
congestions, and then predict the traffic flow which assists in
regulating traffic. The present algorithm for vision-based
detection and counting of vehicles in monocular image
sequences for traffic scenes are recorded by a stationary
camera. The method is based on the establishment of
correspondences between regions and vehicles, as the vehicles
move through the image sequence. Background subtraction is
used which improves the adaptive background mixture model
and makes the system learn faster and more accurately, as well
as adapt effectively to changing environments. The resulting
system robustly identifies vehicles at intersection, rejecting
background and tracks vehicles over a specific period of time.
Real-life traffic video sequences are used to illustrate the
effectiveness of the proposed algorithm
S. N, PD Shenoy, KR Venugopal, and LM Patnaik. Moving vehicle identification using background registration technique for traffic surveillance
Real-time segmentation of moving regions in image
sequences is a fundamental step in many vision systems
including automated visual surveillance and human-machine
interface. In this paper we present a framework for detecting
some important but unknown knowledge like vehicle
identification and traffic flow count. The objective is to
monitor activities at traffic intersections for detecting
congestions, and then predict the traffic flow which assists in
regulating traffic. The present algorithm for vision-based
detection and counting of vehicles in monocular image
sequences for traffic scenes are recorded by a stationary
camera. The method is based on the establishment of
correspondences between regions and vehicles, as the vehicles
move through the image sequence. Background subtraction is
used which improves the adaptive background mixture model
and makes the system learn faster and more accurately, as well
as adapt effectively to changing environments. The resulting
system robustly identifies vehicles at intersection, rejecting
background and tracks vehicles over a specific period of time.
Real-life traffic video sequences are used to illustrate the
effectiveness of the proposed algorithm
HOG, LBP and SVM based Traffic Density Estimation at Intersection
Increased amount of vehicular traffic on roads is a significant issue. High
amount of vehicular traffic creates traffic congestion, unwanted delays,
pollution, money loss, health issues, accidents, emergency vehicle passage and
traffic violations that ends up in the decline in productivity. In peak hours,
the issues become even worse. Traditional traffic management and control
systems fail to tackle this problem. Currently, the traffic lights at
intersections aren't adaptive and have fixed time delays. There's a necessity
of an optimized and sensible control system which would enhance the efficiency
of traffic flow. Smart traffic systems perform estimation of traffic density
and create the traffic lights modification consistent with the quantity of
traffic. We tend to propose an efficient way to estimate the traffic density on
intersection using image processing and machine learning techniques in real
time. The proposed methodology takes pictures of traffic at junction to
estimate the traffic density. We use Histogram of Oriented Gradients (HOG),
Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for
traffic density estimation. The strategy is computationally inexpensive and can
run efficiently on raspberry pi board. Code is released at
https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
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