Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

UTOMATED video surveillance addresses real-time observation of people and vehicles within a busy environment, leading to a description of their actions and interactions. The technical issues include moving object detection and tracking, object classification, human motion analysis, and activity understanding, touching on many of the core topics of computer vision, pattern analysis, and aritificial intelligence. Video surveillance has spawned large research projects in the United States, Europe, and Japan, and has been the topic of several international conferences and workshops in recent years. There are immediate needs for automated surveillance systems in commercial, law enforcement, and military applications. Mounting video cameras is cheap, but finding available human resources to observe the output is expensive. Although surveillance cameras are already prevalent in banks, stores, and parking lots, video data currently is used only “after the fact” as a forensic tool, thus losing its primary benefit as an active, real-time medium. What is needed is continuous 24-hour monitoring of surveillance video to alert security officers to a burglary in progress or to a suspicious individual loitering in the parking lot, while there is still time to prevent the crime. In addition to the obvious security applications, video surveillance technology has been proposed to measure traffic flow, detect accidents on highways, monitor pedestrian congestion in public spaces, compile consumer demographics in shopping malls and amusement parks, log routine maintainence tasks at nuclear facilities, and count endangered species. The numerous military applications include patrolling national borders, measuring the flow of refugees in troubled areas, monitoring peace treaties, and providing secure perimeters around bases and embassies. The 11 papers in this special section illustrate topics and techniques at the forefront of video surveillance research. These papers can be loosely organized into three categories. Detection and tracking involves real-time extraction of moving objects from video and continuous tracking over time to form persistent object trajectories. C. Stauffer and W.E.L. Grimson introduce unsupervised statistical learning techniques to cluster object trajectories produced by adaptive background subtraction into descriptions of normal scene activity. Viewpoint-specific trajectory descriptions from multiple cameras are combined into a common scene coordinate system using a calibration technique described by L. Lee, R. Romano, and G. Stein, who automatically determine the relative exterior orientation of overlapping camera views by observing a sparse set of moving objects on flat terrain. Two papers address the accumulation of noisy motion evidence over time. R. Pless, T. Brodský, and Y. Aloimonos detect and track small objects in aerial video sequences by first compensating for the self-motion of the aircraft, then accumulating residual normal flow to acquire evidence of independent object motion. L. Wixson notes that motion in the image does not always signify purposeful travel by an independently moving object (examples of such “motion clutter” are wind-blown tree branches and sun reflections off rippling water) and devises a flow-based salience measure to highlight objects that tend to move in a consistent direction over time. Human motion analysis is concerned with detecting periodic motion signifying a human gait and acquiring descriptions of human body pose over time. R. Cutler and L.S. Davis plot an object’s self-similarity across all pairs of frames to form distinctive patterns that classify bipedal, quadripedal, and rigid object motion. Y. Ricquebourg and P. Bouthemy track apparent contours in XT slices of an XYT sequence volume to robustly delineate and track articulated human body structure. I. Haritaoglu, D. Harwood, and L.S. Davis present W4, a surveillance system specialized to the task of looking at people. The W4 system can locate people and segment their body parts, build simple appearance models for tracking, disambiguate between and separately track multiple individuals in a group, and detect carried objects such as boxes and backpacks. Activity analysis deals with parsing temporal sequences of object observations to produce high-level descriptions of agent actions and multiagent interactions. In our opinion, this will be the most important area of future research in video surveillance. N.M. Oliver, B. Rosario, and A.P. Pentland introduce Coupled Hidden Markov Models (CHMMs) to detect and classify interactions consisting of two interleaved agent action streams and present a training method based on synthetic agents to address the problem of parameter estimation from limited real-world training examples. M. Brand and V. Kettnaker present an entropyminimization approach to estimating HMM topology and

Computer vision has many applications that were implemented to the existing advance technology. Vehicle tracking and counting in video surveillance is one of them. Vehicle tracking allow the computer to detect and tracking the vehicles that capture through the video surveillance. Afterwards vehicle counting allow to the computer to count all of the vehicles on video. Background Subtraction is one of the method to perform object tracking and counting process for the system. This research implements Pixel Based Adaptive Segmenter Model for Background Subtraction method to perform object tracking and counting. This research used video capture of visual surveillance and vehicles as object to track and count. Result of this research is the system can track and count all vehicles on videos.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

This paper presents a principled way for dealing with occlusions in visual tracking which is a long-standing issue in computer vision but largely remains unsolved. As the major innovation, we develop a learning-based jump-diffusion process to jointly track object locations and estimate their visibility statuses over time. Our method employs in particular a set of jump dynamics to change object's visibility statuses and a set of diffusion dynamics to track objects in videos. Different from the traditional jump-diffusion process that stochastically generates dynamics, we utilize deep policy functions to determine the best dynamic at the present step and learn the optimal policies from raw videos using reinforcement learning methods.Our method is capable of tracking objects with severe occlusions in crowded scenes and thus recovers the complete trajectories of objects that undergo multiple interactions with others. We evaluate the proposed method on challenging video sequences and compare it to alternative methods. Significant improvements are obtained particularly for the videos including frequent interactions or occlusions.

Many researchers are getting attracted in the field of object tracking in video surveillance, which is an important application and emerging research area in image processing. Video tracking is the process of locating a moving object or multiple objects over a time using camera. Due to key features of video surveillance, it has a variety of uses like human-computer interactions, security and surveillance, video communication, traffic control, public areas such as airports, underground stations, mass events, etc. Tracking a target in a cluttered premise is still one of the challenging problems of video surveillance. A sequential flow of moving object detection, its classification, tracking and identifying the behavior completes the processing framework of video surveillance. This paper takes insight into tracking methods, their categorization into different types, focuses on important and useful tracking methods. In this paper, we provide a brief overview of tracking strategies like region based, active contour based, etc with their positive and negative aspects. Different tracking methods are mentioned with detailed description. We review general strategies under literature survey on different techniques and finally stating the analysis of possible research directions.

<p>The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the limitations which are existing nowadays. Although high performance ratio for video object detection and tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to propose a novel video object detection and tracking technique so as to minimize the computational complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature extraction, background subtraction and hole filling. Originally the video clip in the database is split into frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract the multi features from the segmented image and the background image, the feature value thus obtained are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The foreground image is then subjected to morphological operations of erosion and dilation so as to fill the holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus the moving object is tracked in this stage. This method will be employed in MATLAB platform and the outcomes will be studied and compared with the existing techniques so as to reveal the performance of the novel video object detection and tracking technique. </p>

Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.

Detection of objects precisely in video surveillance system is vital for many applications such as abnormal activity detection, congestion analysis, person identification, military fields. For detection of objects, background subtraction and temporal detection methods are used. In background subtraction method moving and static objects which enters into a camera viewing region has been detected. In this method reference frame will be captured during camera initialization. It uses a reference frame as background, detects present frame and takes the difference between two frames. In this paper objects are detected in multiple regions separately from single camera instead of using multiple cameras to monitor different objects in different regions. And also user will have control to select required single or multiple regions to detect the object. This will reduces the overall cost of the system and increases the processing speed of the system if we choose a particular region. Accuracy of the object detection in different regions increases since it allows using different parameters like thresholding separately. So that it monitors different activities at the same time. Also detection of objects based on comparison, object of interest from stored data base is presented using single static camera view with good real time performance.

Autonomous multisensor tracking surveillance system

Video surveillance becomes more and more popular as it is a high-tech method to protect personal safety and public property. Recently it is not the privilege of police and common people can view videos of many public places lively or by on-demand way. Traditional client-server based video streaming solutions incur expensive bandwidth provision cost on the server and are not scale well. Peer-to-Peer (P2P) streaming is a new paradigm to build distributed networking video surveillance applications. Recently, several P2P-TV systems have been deployed to provide live and on-demand video streaming services on the Internet at low server cost. But it is seldom to use p2p streaming in video surveillance. In this paper, we provide the design of large scale video surveillance system client based on p2p streaming. Given the particularity of video surveillance, such as high churn and the heterogeneity of user access network and delay-tolerant, we construct the end-hosts into mesh application layer topology and adopt pull-push mode to delivery the data. Based on test on our previous implement of e-Touch video surveillance system, it shows that it is feasibly to realize video client using p2p streaming technology and simulation results shows single streaming media server can support tens of thousands of users simultaneously participating in the system.

Real time object tracking is an essential part of surveillance and robot applications. The performance of any object tracking system depends on its accuracy and its ability to deal with various sizes of objects, for better results the tracking should happen at a high speed. Object tracking can be defined as the process of segmenting the region of interest from the video sequence and keeping track of the motion in order to extract useful information. The basic fundamentals of video surveillance such as object detection, tracking and recognition using multiple cameras. Whenever an object enters a video frame, it is detected using background subtraction and the detected objects are tracked with a novel Bayesian Kalman filter with simplified Gaussian mixture (BKF-SGM). Object recognition algorithm is applied using Gait method which is employed to support multiple object tracking with its robust performance. Processing of image is done in MATLAB to get detection, tracking and recognized results.

Recently, generation of novel views from images acquired by multiple cameras has been investigated in the fields of virtual and mixed reality. Most conventional methods need some assumptions about the scene such as a static scene and limited positions of objects. We propose a new method for generating novel view images of a dynamic scene with a wide view, which does not depend on the scene. The images acquired from omni-directional cameras are first divided into static regions and dynamic regions. The novel view images are then generated by applying a morphing technique to static regions and by computing visual hulls for dynamic regions in realtime. In experiments, we show that a prototype system can generate novel view images in real-time from live video streams.

Face tracking has become an active area of research it is very much helpful in applications such as video surveillance. The factors like illumination discrepancy, face pose variations, occlusions, and background clutter makes face tracking a challenging issue. Even though, a number of related work have been proposed to assist face tracking in unrestrained videos, the existing approaches may not be productive in video surveillance applications. This is mainly because of the fact that low-resolution face comprises of lesser informative data, and main transformations in illumination on the face make the tracking unproductive. In order to overcome this issue, this paper proposes a novel algorithm for illumination normalization and it is combined with the GLF for face tracking for improving the overall performance of the tracker system. Experimental results show that the proposed GLF-based tracker works well under significant clarification changes and outperforms many existing tracking algorithms.

Tracking multiple objects in parallel is a difficult task, especially if instances are interacting and occluding each other. To alleviate the arising problems multiple camera views can be taken into account, which, however, increases the computational effort. Evoking the need for very efficient methods, often rather simple approaches such as background subtraction are applied, which tend to fail for more difficult scenarios. Thus, in this work, we introduce a powerful multi-instance tracking approach building on Hough Forests. By adequately refining the time consuming building blocks, we can drastically reduce their computational complexity without a significant loss in accuracy. In fact, we show that the test time can be reduced by one to two orders of magnitude, allowing to efficiently process the large amount of image data coming from multiple cameras. Furthermore, we adapt the pre-trained generic forest model in an online manner to train an instance-specific model, making it well suited for multi-instance tracking. Our experimental evaluations show the effectiveness of the proposed efficient Hough Forests for object detection as well as for the actual task of multi-camera tracking.KeywordsRandom ForestOnline LearningGround PlaneImage PatchFeature ChannelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Multi-target tracking is a active research field nowadays due to its wide practical applicability in video processing. While talking about Multi-target tracking, ‘multi-target occlusion’ is a common problem that needs to be addressed. Lots of work has been done using multiple cameras for handling ‘multitarget occlusion’; however most of them require camera calibration parameters that make them impractical for outdoor video surveillance applications. The main focus of this paper is to reduce the dependency on camera calibration for multiple camera collaboration. In this perspective Gale-Shapley algorithm (GSA) has been used for finding stable matching between two or more camera views, while more robustness on tracking of objects has been ensured by combining multiple cues such object’s boundary information of the object with color histogram. Efficient tracking of object ensures proficient reckoning of target depicting parameter (i.e. apparent color, height and width information of the object) as a consequence better camera collaboration. The simulation results prove the validity of our approach.Keywordssurveillancemultiple camera trackingmulti-people trackingparticle filtering