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The number of video surveillance cameras has increased by a large amount in recent years. There is therefore a need to process the captured videos such that human operators can quickly review the activities recorded by a camera over a long period of time. We propose in this paper an approach for producing video summaries, an abbreviated video preserving the important elements of interest. We introduce...
Extracting the motion patterns from videos is a basic task in video surveillance and has become an active research area. In this paper, we propose a novel approach for discovering motion patterns in a scene observed by one or two cameras. The chaos theory is employed to compute the chaotic invariant features (CIFs) after obtaining all the trajectories. The CIFs and other features are combined to a...
This paper examines the problem of motion segmentation by analyzing trajectories with statistical approach. We propose a statistical framework for motion segmentation, which makes no assumption on camera motion, camera model, number of moving objects and scene complexity. Long range trajectories are traced across frames and clustered by DTW metric. Various descriptors can be used to construct a weighted...
A method for online identification of group of moving objects in the video is proposed in this paper. This method at each frame identifies group of tracked objects with similar local instantaneous motion pattern using spectral clustering on motion similarity graph. Then, the output of the algorithm is used to detect the event of more than two object moving together as required by PETS2015 challenge...
In this paper, a novel global abnormal event detection algorithm is proposed for multiple disjoint synchronous camera network. We treat detecting unusual global events as discovering context-incoherent patterns through learning temporal dependencies between distributed local activities observed within and across camera views. Trajectories are firstly extracted using mean-shift approach in each camera...
We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera...
With the rapid development of the camera industry, surveillance systems become more and more popular in our daily life. However, it is very time-consuming to find out specific persons or objects from a mass of surveillance videos with long duration. For efficient browsing surveillance videos, numerous researchers are devoted to eliminating the inherent spatiotemporal redundancy for video synopsis...
Vehicle tracking is an important topic in computer vision. With the development of Intelligent Transportation System (ITS), research of vehicle tracking has been more and more active. Most traditional vehicle tracking algorithms are based on background model, which are easily affected by light and perspective transform, and have difficulty to solve occlusion and camera motion. The proposed vehicle...
This work proposes algorithms to control the trajectory of a team of cameras for video surveillance. We consider a chain of cameras installed in an environment. These cameras are used to detect smart intruders, who are aware of the cameras' configuration at each time instant, and who schedule their motion to avoid detection, if possible. For this problem setup, we first obtain a lower bound on the...
We introduce the Longterm Observation of Scenes (with Tracks) dataset. This dataset comprises videos taken from streaming outdoor webcams, capturing the same half hour, each day, for over a year. LOST contains rich metadata, including geolocation, day-by-day weather annotation, object detections, and tracking results. We believe that sharing this dataset opens opportunities for computer vision research...
This paper considers the problem of tracking a variable number of objects through a surveillance site monitored by multiple cameras with slightly overlapping field-of-views. To this end, we propose to cluster tracklets generated by a commercially available single-camera video-analysis algorithm which is solely based on the position of objects. A first contribution of this paper is the proposal of...
In this paper, we investigate the applicability of the newly proposed data clustering method, affinity propagation, in feature points clustering and the task of vehicle detection and tracking in road traffic surveillance. We propose a model-based temporal association scheme and novel preprocessing and postprocessing operations which together with affinity propagation make a quite successful method...
We present a novel approach for discovering directed intention-driven pedestrian activities across large urban areas. The proposed approach is based on a mutual information co-clustering technique that simultaneously clusters trajectory start locations in the scene which have similar distributions across stop locations and vice-versa. The clustering assignments are obtained by minimizing the loss...
We propose a method for segmenting an arbitrary number of moving objects using the geometry of 6 points in 2D images to infer motion consistency. This geometry allows us to determine whether or not observations of 6 points over several frames are consistent with a rigid 3D motion. The matching between observations of the 6 points and an estimated model of their configuration in 3D space is quantified...
In this paper a technique is presented to estimate the traffic intensity for each lane. This method does not require background estimation or even the identification and tracking of individual vehicles. It requires only the identification of each lane and the estimation of a bird eye view of the highway using a rectification method. To each rectified lane, an intensity profile is computed along the...
As the collection of moving object data become much easier, event-based outlier detection such as congestion in trajectory data are becoming increasingly attractive to data mining community. Most of the existing methods only perform the trajectory outlier detection on the spatial information. In this paper, a framework for congestion outlier detection with clustering method was proposed. Trajectory...
Modeling and predicting human and vehicle motion is an active research domain. Due to the difficulty of modeling the various factors that determine motion (e.g., internal state and perception), this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g., camera and laser scanner), and then using...
The paper describes a general platform for live video analysis. The first stage of the platform is to build a topological scene description by learning the location of nodes (i.e. zones), which are called points of interest. There are two kinds of points of interest, the entry-exit zones (areas where moving object appear and disappear in the scene) and the stopping zones (areas where the moving objects...
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