Features-based moving objects tracking for smart video surveillances: A review

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance

Formalization of People and Crowd Detection and Tracking in Video

Оne of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of people and crowd in video. The approach for tracking multiple people on video sequences for indoor and outdoor is described. The results of experiments for video sequences obtained using a stationary and moving video camera are presented

Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing

In this paper, we present a new method for detecting road users in an urban environment which leads to an improvement in multiple object tracking. Our method takes as an input a foreground image and improves the object detection and segmentation. This new image can be used as an input to trackers that use foreground blobs from background subtraction. The first step is to create foreground images for all the frames in an urban video. Then, starting from the original blobs of the foreground image, we merge the blobs that are close to one another and that have similar optical flow. The next step is extracting the edges of the different objects to detect multiple objects that might be very close (and be merged in the same blob) and to adjust the size of the original blobs. At the same time, we use the optical flow to detect occlusion of objects that are moving in opposite directions. Finally, we make a decision on which information we keep in order to construct a new foreground image with blobs that can be used for tracking. The system is validated on four videos of an urban traffic dataset. Our method improves the recall and precision metrics for the object detection task compared to the vanilla background subtraction method and improves the CLEAR MOT metrics in the tracking tasks for most videos

Multi camera soccer player tracking

Now a day’s spread of super computers, existing of high resolution and low-priced video cameras, and increasing the computerized video analysis has made more curiosity in tracking algorithms. Automatic identification and tracing of multiple moving objects through video scene is an interesting field of computer visualization. Identification and tracking of multiple people is a vital and challenging task for many applications like human-computer interface, video communication, security application and surveillance system. Various researchers offer various algorithms but none of this was work properly to distinguish the players automatically when creating occlusion. The first step to tracking multiple objects in video sequence is detection. Background subtraction is a very popular and effective method for foreground detection (assuming that background should be stationary). In this thesis we apply various background subtraction methods to tackle the difficulties like changing illumination condition, background clutter and camouflage. The method we propose to overcome this problem is operates the background subtraction by calculating the Mahalanobis distances. The second step to track multiple moving objects in soccer scene by using particle filters method that estimate the non-Gaussian, non-linear state-space model, which is a multi-target tracking method. These methods are applied on real soccer video sequences and the result show that it is successfully track and distinguish the players. After tracking is done by using multi camera views, we collecting the data from all cameras and creating geometrical relationship between cameras called Homography

Real time pedestrian detection and tracking for driver assistance systems

Real time pedestrian detection and tracking is considered as a critical application. Night time driving is more risky as compared to day time driving because of poor visibility especially in the case of senior citizens. While traditional methods of segmentation using thresholding, background subtraction and background estimation provide satisfactory results to detect single objects, noise is produced in case of multiple objects and in poor lighting conditions. To overcome these difficulties, a new method is proposed for detecting and tracking multiple moving objects on night-time lighting conditions. The method is performed by integrating both the wavelet-based contrast change detector and locally adaptive thresholding scheme. In the initial stage, to detect the potential moving objects contrast in local change over time is used. To suppress false alarms motion prediction and spatial nearest neighbor data association are used. A latest change detector mechanism is implemented to detect the changes in a video sequence and divide the sequence into scenes to be encoded independently. Using the change detector algorithm (CD), it was efficient enough to detect abrupt cuts and help divide the video file into sequences. With this we get a sufficiently good output with less noise. But in some cases noise becomes prominent. Hence, a method called correlation is used which gives the relation between two consecutive frames which have sufficient difference to be used as current and previous frame. This gives a way better result in poor light condition and multiple moving objects

PERFORMANCE METRICS IN VIDEO SURVEILLANCE SYSTEM

Video surveillance is an active research topic in computer vision. One of the areas that are being actively researched is on the abilities of surveillance systems to track multiple objects over time in occluded scenes and to keep a consistent identity for each target object. These abilities enable a surveillance system to provide crucial information about moving objects behaviour and interaction. This survey reviews the recent developments in moving object detection and also different techniques and approaches in multiple objects tracking that have been developed by researchers. The algorithms and filters that can be incorporated in tracking multiples object to solve the occluded and natural busy scenes in surveillance systems are also reviewed in this paper. This survey is meant to provide researchers in the field with a summary of progress achieved up to date in multiple moving objects tracking. Despite recent progress in computer vision and other related areas, there are still major technical challenges that need to be solved before reliable automated video surveillance system can be realized