V. CONCLUSION & FUTURE WORKMoving object detection is a basis for a number ofimportant applications such as real-time surveillance andvisual tracking. However, it is computationally expensive andresource hungry. Here, two cost-effective algorithmsbackground subtraction and sobel filtering are combined forreal-time moving object detection. The modified motionsegmentation technique considerably reduces the detectiondelay and memory usage, while effectively handling randomnoise.
The algorithm do not rely on any special hardware. Sothe combined algorithm is more appropriate for embeddedsystems and portable devices. Also the work is extended formotorcycle recognition in traffic videos. Experimental resultsdemonstrate that the system was capable to identify multiplemotorbikes in normal urban and highway road conditions. Ourfuture goal is to extend the project for helmet detection onmotorcyclists. We are also looking to build the hardwareprototype to test our software on real world scenarios anddifferent weather conditions instead of testing on pre-recordedvideos.
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