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Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video
"... Abstract—This paper presents two different types of visual activity analysis modules based on vehicle tracking. The highway monitoring module accurately classifies vehicles into eight different types and collects traffic flow statistics by leveraging tracking information. These statistics are contin ..."
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Cited by 43 (20 self)
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Abstract—This paper presents two different types of visual activity analysis modules based on vehicle tracking. The highway monitoring module accurately classifies vehicles into eight different types and collects traffic flow statistics by leveraging tracking information. These statistics are continuously accumulated to maintain daily highway models that are used to categorize traffic flow in real time. The path modeling block is a more general analysis tool that learns the normal motions encountered in a scene in an unsupervised fashion. The spatiotemporal motion characteristics of these motion paths are encoded by a hidden Markov model. With the path definitions, abnormal trajectories are detected and future intent is predicted. These modules add realtime situational awareness to highway monitoring for high-level activity and behavior analysis. Index Terms—Anomaly detection, comparative flow analysis, highway efficiency, real-time tracking analysis, trajectory learning and prediction, vehicle type classification. I.
ABSTRACT AUTOMATIC ESTIMATION OF VEHICLE SPEED FROM UNCALIBRATED VIDEO SEQUENCES
, 2005
"... Video sequences of road and traffic scenes are currently used for various purposes, such as studies of the traffic character of freeways. The task here is to automatically estimate vehicle speed from video sequences, acquired with a downward tilted camera from a bridge. Assuming that the studied roa ..."
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Cited by 5 (0 self)
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Video sequences of road and traffic scenes are currently used for various purposes, such as studies of the traffic character of freeways. The task here is to automatically estimate vehicle speed from video sequences, acquired with a downward tilted camera from a bridge. Assuming that the studied road segment is planar and straight, the vanishing point in the road direction is extracted automatically by exploiting lane demarcations. Thus, the projective distortion of the road surface can be removed allowing affine rectification. Consequently, given one known ground distance along the road axis, 1D measurement of vehicle position in the correctly scaled road direction is possible. Vehicles are automatically detected and tracked along frames. First, the background image (the empty road) is created from several frames by an iterative per channel exclusion of outlying colour values based on thresholding. Next, the subtraction of the background image from the current frame is binarized, and morphological filters are employed for vehicle clustering. At the lowest part of vehicle clusters a window is defined for normalised cross-correlation among frames to allow vehicle tracking. The reference data for vehicle speed came from rigorous 2D projective transformation based on control points (which had been previously evaluated against GPS measurements). Compared to these, our automatic approach gave a very satisfactory estimated accuracy in vehicle speed of about ±3 km/h. 1.
Compact Vehicular Trajectory Encoding (extended version)
"... Abstract—Many applications in vehicular communications require the collection of vehicular position traces. So far this has been done by recording and transmitting unencoded position samples. Depending on the frequency and resolution of these samples, the resulting data may be very large, consuming ..."
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Cited by 4 (3 self)
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Abstract—Many applications in vehicular communications require the collection of vehicular position traces. So far this has been done by recording and transmitting unencoded position samples. Depending on the frequency and resolution of these samples, the resulting data may be very large, consuming significant resources for storage and transmission. In this paper, we propose a method based on two-dimensional cubic spline interpolation that is able to reduce the amount of the measurement data significantly. Our approach allows for a configurable accuracy threshold and performs its task in O(n 3). We evaluate our approach with real vehicular GPS movement traces and show that it is able to reduce the volume of the measurement set by up to 80 % for an accuracy threshold of 20 centimeters. I.
RETRIEVAL OF VEHICLE TRAJECTORIES AND ESTIMATION OF LANE GEOMETRY USING NON-STATIONARY TRAFFIC SURVEILLANCE CAMERAS
"... A tracking system is presented for obtaining accurate vehicle trajectories using uncalibrated traffic surveillance cameras. Techniques for indexing and retrieval of vehicle trajectories and estimation of lane geometry are also presented. An algorithm known as Predictive Trajectory Merge-and-Split (P ..."
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Cited by 2 (0 self)
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A tracking system is presented for obtaining accurate vehicle trajectories using uncalibrated traffic surveillance cameras. Techniques for indexing and retrieval of vehicle trajectories and estimation of lane geometry are also presented. An algorithm known as Predictive Trajectory Merge-and-Split (PTMS) is used to detect partial or complete occlusions during object motion. This hybrid algorithm is based on the constant acceleration Kalman filter and a set of simple heuristics for temporal analysis. The resulting vehicle trajectories are modeled using variable low-order polynomials. A comparative evaluation of several distance metrics used in trajectory cluster analysis, indexing and retrieval is also presented. We propose some changes to metrics presented in previous work and make a comparative study with a modified form of the Hausdorff distance. Some preliminary results are presented on the estimation of lane geometry through K-means clustering of individual vehicle trajectories using the proposed metrics. An advantage of our approach is that estimation of lane geometry can be performed with non-stationary, uncalibrated traffic cameras in real time. 1.
Article Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera
, 2010
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Motion trajectory clustering for video retrieval using spatio-temporal approximations
- in Visual Information and Information Systems
"... Abstract. A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. The trajectories are treated as motion time series and represented either by least squares or Chebyshev polynomial approximations. Trajectory clustering i ..."
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Abstract. A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. The trajectories are treated as motion time series and represented either by least squares or Chebyshev polynomial approximations. Trajectory clustering is then carried out to discover patterns of similar object motion behaviour. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this manner leads to efficiency gains over ex-isting approaches that use point-based flow vectors to represent the whole tra-jectory as input vector. Experiments on two different motion datasets – vehicle tracking and pedestrian surveillance- demonstrate the effectiveness of our ap-proach. Applications to motion data mining in video surveillance databases are envisaged. 1
Development of a Client-Server System for 3D Scene Change Detection
, 2013
"... In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to ..."
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In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change cor-rectly.
REAL TIME SPEED ESTIMATION FROM MONOCULAR VIDEO
"... In this paper, detailed studies have been performed for developing a real time system to be used for surveillance of the traffic flow by using monocular video cameras to find speeds of the vehicles for secure travelling are presented. We assume that the studied road segment is planar and straight, t ..."
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In this paper, detailed studies have been performed for developing a real time system to be used for surveillance of the traffic flow by using monocular video cameras to find speeds of the vehicles for secure travelling are presented. We assume that the studied road segment is planar and straight, the camera is tilted downward a bridge and the length of one line segment in the image is known. In order to estimate the speed of a moving vehicle from a video camera, rectification of video images is performed to eliminate the perspective effects and then the interest region namely the ROI is determined for tracking the vehicles. Velocity vectors of a sufficient number of reference points are identified on the image of the vehicle from each video frame. For this purpose sufficient number of points from the vehicle is selected, and these points must be accurately tracked on at least two successive video frames. In the second step, by using the displacement vectors of the tracked points and passed time, the velocity vectors of those points are computed. Computed velocity vectors are defined in the video image coordinate system and displacement vectors are measured by the means of pixel units. Then the magnitudes of the computed vectors in the image space are transformed to the object space to find the absolute values of these magnitudes. The accuracy of the estimated speed is approximately ± 1-2 km/h. In order to solve the real time speed estimation problem, the authors have written a software system in C++ programming language. This software system has been used for all of the computations and test applications.
