Abstract – The advance of technology makes video acquisition devices better and less costly, thereby increasing the number of applications that can effectively utilize digital video. Compared to still images, video sequences provide more information about how objects change over time. Different applications can be developed through detection of moving objects in video. We have identified two major applications
of the Video Object tracking algorithm. The algorithm can be used to estimate the speed of the moving object and can also be used to optimize the storage space of the recorded video in the surveillance system.
Index Terms – Segmentation, optimization, speed estimation, sobel algorithm, rectangularization, static camera, object tracking.
INTRODUCTION
Video tracking is the process of locating a moving object (or several ones) in time using a camera. An algorithm analyses the video frames and outputs the location of moving
targets within the video frame. The main difficulty in video tracking is to associate target
locations in consecutive video frames, especially when the objects are moving fast relative to the frame rate. Here, video tracking systems usually employ a motion model which
describes how the image of the target might change for different possible motions of the object to track.
The Video Object Tracking (VOT) system operates in the controlled campus environment of Infosys Technologies Limited. The system takes the video as input from the single static camera and does the processing. It starts recording the video after the motion is detected and also computes the speed of moving object.
PROBLEM DEFINITION
Moving objects need to be tracked and analyzed in videos. We are working in a controlled campus like environment where a single static camera is used to record the
objects motion. This reduces the complexities involved with moving or multiple cameras. After the object’s motion is detected it will be used to develop different applications like
peed estimation and storage space optimization.
RELATED WORK
There have been few algorithms and techniques used to track objects through video devices. Like tracking moving objects through video camera, semi-automatic object tracking
in video etc. Each of the methods work on different algorithms to track the objects. Every method has its advantages and disadvantages. Listing the techniques briefly –
I. Moving Object Tracking in Video [1]
The algorithm first separates the moving objects from the background in each frame. Then, four sets of variables are computed based on the positions, the sizes, the grayscale
distributions and the presence of textures of the objects. A rule-based method is developed to track the objects between frames, based on the values of the variables.
II. Real-Time Object tracking from a moving camera [2]
The algorithm is based on describing the displacement of a point as a probability distribution over a matrix of possible displacements. A small set of randomly selected points is used
to compute the registration parameters. Moving object detection is based on the consistency of the probabilistic displacement of image points with the global image motion.
III. Semi-auto object tracking in video sequences [3]
A method is presented for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results
producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the
sequence modeling the a posteriori probabilities of a set of features such as color, position and motion, depth, etc.
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