Video Mining using LIM Based Clustering and Self Organizing Maps

AbstractVideo mining has grown as an energetic research area and given incremental concentration in recent years due to impressive and rapid raise in the volume of digital video databases. The aim of this research work is to find out new objects in videos. This work proposes a novel approach for video mining using LIM based clustering technique and self organizing maps to recognize novelty in the frames of video sequence. The proposed work is designed and implemented on MATLAB. It is tested with the sample videos and provides promising results. And it is suitable for day to day video mining applications and object detection systems including remote video surveillance in defense for national and international border tracking

Techniques and Methods for Detection and Tracking of Moving Object in a Video Suraj Pramod Patil

ABSTRACT: The use of video is becoming prevalent in many applications such as monitoring of traffic, detection of pedestrians, identification of anomalous behaviour in a parking lot or near an ATM, etc. While a single image provides a snapshot of a scene, the different frames of a video taken over time registers the dynamics in the scene, making it possible to capture motion in the sequence. Most of the methods include object segmentation using background subtraction. The tracking strategies use different methodologies like Meanshift, Kalman filter, Particle filter etc. Here, classification of the tracking methods is done, and a detailed description is provided of representative methods in each group. Research works on object detection and tracking in videos. The definition and tasks of object detection and tracking are first described, and the important applications are mentioned

Metadata extraction and organization for intelligent video surveillance system

Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), 2010, p. 489-494The research for metadata extraction originates from the intelligent video surveillance system, which is widely used in outdoor and indoor environment for the aims of traffic monitor, security guard, and intelligent robot. Various features are extracted from the surveillance image sequences such as target detection, target tracking, object's shape and activities. However, the trend of more and more features being used and shared in video surveillance system calls for more attention to bridge the gap between specific analysis algorithms and enduser's expectation. This paper proposes a three-layer object oriented model to extract the surveillance metadata including shape, motion speed, and trajectory of the object emerging in image sequence. Meanwhile, the high-level semantic metadata including entry/exit point, object duration time is organized and stored which are provided for the further end-user queries. The paper also presents the experiment results in different indoor and outdoor surveillance scenarios. At last, a comparative analysis with another traditional method is presented. © 2010 IEEE.published_or_final_versio

Detection-aware multi-object tracking evaluation

Master Universitario en Deep Learning for Audio and Video Signal ProcessingMulti-Object Tracking (MOT) is a hot topic in the computer vision field. It is a complex task that requires a detector, to identify objects, and a tracker, to follow them. It is useful for self-driving, surveillance and robot vision, between others, where research teams and companies are trying to improve their models. In order to determine which model performs better, they are scored using tracking metrics. In this thesis we experiment with MOT metrics aware of detection by using correlation matrices. By analyzing the results, we realize that tracking metrics incur in certain issues that prevent them for correctly reflecting tracking performance. The performance of the detector is relevant when scoring tracking models. The problem observed is that tracking metrics weigh differently elements that evaluate detection performance. Thus, improving one detector’s aspect with a high weight in the MOT metric will significantly improve the tracker’s score, but not necessarily indicating the amount of effort done by the tracker. That is, trackers are not evaluated in a balanced way. In order to solve this issue with the tracker scoring, we present a new multi-object tracking metric, based on the effort done by the tracker given a certain set of detections. This effort is calculated based on the improvement of bounding boxes over the ones given by the detector and the precision to keep the trace of the objects in a sequence. The metric has been tested for two widely employed datasets and shows us its reliability scoring tracking metrics. Also, it do not incur in the problem presented above

Video object segmentation and tracking.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2005One of the more complex video processing problems currently vexing researchers is that of object segmentation. This involves identifying semantically meaningful objects in a scene and separating them from the background. While the human visual system is capable of performing this task with minimal effort, development and research in machine vision is yet to yield techniques that perform the task as effectively and efficiently. The problem is not only difficult due to the complexity of the mechanisms involved but also because it is an ill-posed problem. No unique segmentation of a scene exists as what is of interest as a segmented object depends very much on the application and the scene content. In most situations a priori knowledge of the nature of the problem is required, often depending on the specific application in which the segmentation tool is to be used. This research presents an automatic method of segmenting objects from a video sequence. The intent is to extract and maintain both the shape and contour information as the object changes dynamically over time in the sequence. A priori information is incorporated by requesting the user to tune a set of input parameters prior to execution of the algorithm. Motion is used as a semantic for video object extraction subject to the assumption that there is only one moving object in the scene and the only motion in the video sequence is that of the object of interest. It is further assumed that there is constant illumination and no occlusion of the object. A change detection mask is used to detect the moving object followed by morphological operators to refine the result. The change detection mask yields a model of the moving components; this is then compared to a contour map of the frame to extract a more accurate contour of the moving object and this is then used to extract the object of interest itself. Since the video object is moving as the sequence progresses, it is necessary to update the object over time. To accomplish this, an object tracker has been implemented based on the Hausdorff objectmatching algorithm. The dissertation begins with an overview of segmentation techniques and a discussion of the approach used in this research. This is followed by a detailed description of the algorithm covering initial segmentation, object tracking across frames and video object extraction. Finally, the semantic object extraction results for a variety of video sequences are presented and evaluated

Robust Visual Tracking Using Illumination Invariant Features in Adaptive Scale Model

When entering into the realm of Computer Vision, the first thing which comes in to mind is Visual tracking. Visual tracking by far comes into one of the most actively investigated research areas because of the fact that it has an extensive collection of applications in areas such as activity recognition, surveillance, motion analysis and as well as human computer interaction. Some serious challenges of this area which still create hindrance in achieving 100% accuracy are abrupt appearance and pose changes of an object along with its background blockage due to blockages called occlusion, illumination and lighting variances and changes in scale of target object in the frames. Moreover, diverse algorithms had been proposed for the resolution of said issue. Now in such cases, if we study the statistical analysis of correlation between two frames in a certain video, it can be efficiently utilized to get the most exact location of the targeted object. The algorithms in existence today do not completely exploit a strong spatio-temporal relationship that very often occurs between the two successive frames in a video sequence. Recent advances in correlation-based tracking systems have been proposed to address the problem in successive frames. In this thesis a very simple yet quite speedy and robust algorithm that in actual brings all the relevant information used for Visual Tracking. Two of the Models proposed are the “Locality Sensitive Histogram” and “Discriminative Scale Tracking Method”. These are robust enough to the variations which are based on appearance which are normally presented by blockage, pose, illumination and lighting variations alike. A scheme is proposed called scale adaptation which is very much clever to adapt variations of targeted scale in the most efficient manner. The Discriminative Scale Tracking Method is used for detection as well as scale change ultimately resulting in an effective tracking method in the end. Various different experiments with the best algorithms have demonstrated on challenging sequences that the suggested methodology attains promising results as far as robustness, accuracy, and speed is concerned

Adaptive object segmentation and tracking

Efficient tracking of deformable objects moving with variable velocities is an important current research problem. In this thesis a robust tracking model is proposed for the automatic detection, recognition and tracking of target objects which are subject to variable orientations and velocities and are viewed under variable ambient lighting conditions. The tracking model can be applied to efficiently track fast moving vehicles and other objects in various complex scenarios. The tracking model is evaluated on both colour visible band and infra-red band video sequences acquired from the air by the Sussex police helicopter and other collaborators. The observations made validate the improved performance of the model over existing methods. The thesis is divided in three major sections. The first section details the development of an enhanced active contour for object segmentation. The second section describes an implementation of a global active contour orientation model. The third section describes the tracking model and assesses it performance on the aerial video sequences. In the first part of the thesis an enhanced active contour snake model using the difference of Gaussian (DoG) filter is reported and discussed in detail. An acquisition method based on the enhanced active contour method developed that can assist the proposed tracking system is tested. The active contour model is further enhanced by the use of a disambiguation framework designed to assist multiple object segmentation which is used to demonstrate that the enhanced active contour model can be used for robust multiple object segmentation and tracking. The active contour model developed not only facilitates the efficient update of the tracking filter but also decreases the latency involved in tracking targets in real-time. As far as computational effort is concerned, the active contour model presented improves the computational cost by 85% compared to existing active contour models. The second part of the thesis introduces the global active contour orientation (GACO) technique for statistical measurement of contoured object orientation. It is an overall object orientation measurement method which uses the proposed active contour model along with statistical measurement techniques. The use of the GACO technique, incorporating the active contour model, to measure object orientation angle is discussed in detail. A real-time door surveillance application based on the GACO technique is developed and evaluated on the i-LIDS door surveillance dataset provided by the UK Home Office. The performance results demonstrate the use of GACO to evaluate the door surveillance dataset gives a success rate of 92%. Finally, a combined approach involving the proposed active contour model and an optimal trade-off maximum average correlation height (OT-MACH) filter for tracking is presented. The implementation of methods for controlling the area of support of the OT-MACH filter is discussed in detail. The proposed active contour method as the area of support for the OT-MACH filter is shown to significantly improve the performance of the OT-MACH filter's ability to track vehicles moving within highly cluttered visible and infra-red band video sequence