Paper Info
Title
Design and implementation of novel image segmentation and BLOB detection algorithm for real-time video surveillance using DaVinci processor
Abstract
A video surveillance system is primarily designed to track key objects, or people exhibiting suspicious behavior, as they move from one position to another and record it for possible future use. The critical parts of an object tracking algorithm are object segmentation, image clusters detection, and identification and tracking of these image clusters. The major roadblocks of the tracking algorithm arise due to abrupt object shape, ambiguity in number and size of objects, background and illumination changes, noise in images, contour sliding, occlusions and real-time processing. This paper will explain a solution of the object tracking problem, in 3 stages: In the first stage, design a novel object segmentation and background subtraction algorithm, These algorithm will take care of salt pepper noise, and changes in scene illumination. In the second stage, solve the abrupt object shape problems, objects size and count various objects present , using image clusters detected and identified by the BLOBs (Binary Large OBjects) in the image frame. In the third stage, design a centroid based tracking method, to improve robustness w.r.t occlusion and contour sliding. A variety of optimizations, both at algorithm level and code level, are applied to the video surveillance algorithm. At code level optimization mechanisms significantly reduce memory access, memory occupancy and improved operation execution speed. Object tracking happens in real-time consuming 30 frames per second(fps) and is robust to occlusion, contour sliding, background and illumination changes. Execution time for different blocks of this object tracking algorithm were estimated and the accuracy of the detection was verified using the debugger and the profiler, which will provided by the TI(Texas Instrument) Code Composer Studio (CCS). We demonstrate that this algorithm, with code and algorithm level optimization on TIs DaVinci multimedia processor (TMS320DM6437), provides at least two times speedup and is a- le to track a moving object in real-time as compared to without optimization.
Year
DOI
Venue
2014
10.1109/ICACCI.2014.6968360
Advances in Computing, Communications and Informatics
Keywords
Field
DocType
edge detection,image denoising,image segmentation,microprocessor chips,object detection,object tracking,pattern clustering,video signal processing,video surveillance,BLOB detection algorithm,DaVinci multimedia processor,TMS320DM6437,algorithm level,background changes,background subtraction algorithm,binary large objects,centroid based tracking method,code level,contour sliding,image clusters detection,image clusters identification,image clusters tracking,image frame,image segmentation,images noise,memory access,memory occupancy,object segmentation,object shape,object tracking algorithm,objects size,occlusions,operation execution speed,optimization mechanisms,real-time processing,real-time video surveillance,salt pepper noise,scene illumination changes,BLOB,Background Subtraction DaVinci Processor,Centroid Based,Optimization,Segmentation,Tracking
Computer vision,Scale-space segmentation,Feature detection (computer vision),Corner detection,Image texture,Computer science,Segmentation-based object categorization,Image segmentation,Video tracking,Blob detection,Artificial intelligence
Conference
Citations 
PageRank 
References 
2
0.40
3
Authors
1
Name
Order
Citations
PageRank
Badri Narayana Patro164.52