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Object Tracking Using Radial Basis Function Networks No ratings yet.


The applications of visual tracking are broad in scope ranging from surveillance and monitoring to smart rooms. A robust object-tracking algorithm using Radial Basis Function (RBF) networks has been implemented using OpenCV libraries.

The pixel-based color features are used to develop classifiers. The algorithm has been tested on various video samples under different conditions, and the results are analyzed.

The objective of tracking is to follow the target object in successive video frames. The major utility of such algorithm is in the design of video surveillance system to tackle terrorism. For instance, large-scale surveillance might have played a crucial role in preventing (or tracking the trails of terrorism) 26/11 terrorist attacks in Mumbai, many bomb blasts in Kashmir, North-east Indian region, and other parts of India.

It is important to have a robust object-tracking algorithm. Since neural network framework does not require any assumptions on structures of input data, they have been used in the field of pattern recognition, image analysis, etc. The Radial Basis Function (RBF) based neural network is one of many ways to build classifiers. A robust algorithm for object tracking using RBF networks was described in the paper [1]. We have implemented that algorithm using OpenCV libraries so that this module can be integrated into a large surveillance system.

Object Tracking

Object tracking is an important task within the field of computer vision. The growth of high-performance computers, the availability of high quality yet inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms.

There are three key steps in video analysis: detection of interesting moving objects, tracking of such objects from frame to frame, and analysis of tracks to recognize their behavior. The object tracking is pertinent to the tasks of:

 Motion-based recognition, that is, human identification based on gait, automatic object detection, etc.

 Automated surveillance, that is, monitoring a scene to detect suspicious activities or unlikely events

 Video indexing, that is, automatic annotation and retrieval of the videos in multimedia databases

 Human-computer interaction, that is, gesture recognition, eye gaze tracking for data input to computers, etc.

 Traffic monitoring, that is, real-time gathering of traffic statistics to direct traffic flow

 Vehicle navigation that is, video-based path planning and obstacle avoidance capabilities

In its simplest form, tracking can be defined as the problem of estimating the trajectory of an object in the image plane as it moves around a scene. A tracker assigns consistent labels to the tracked objects in different frames of a video. Additionally, depending on the tracking domain, a tracker can also provide object-centric information, such as orientation, area, or shape of an object. Tracking objects can be complex due to:

 Loss of depth information
 Noise in images,
 Complex object motion,
 Non-rigid or articulated nature of objects,
 Partial and full object occlusions,
 Complex object shapes,
 Scene illumination changes, and
Real-time processing requirements.

Project Done by A. Prem Kumar[a], T. N. Rickesh[b], R. Venkatesh Babu[c ], R. Hariharan[d]

[a] – Indian Institute of Technology Bombay [c] – Video analytics consultant
[b]- National Institute of Technology Karnataka, Surathkal [d] – Junior scientist, Flosolver

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