Enhancing accuracy to track and count multiple vehicles from surveillance video using back propagation neural network over oriented and rotated brief algorithm

The project’s goal is to identify vehicles, measure count and provide results as the vehicle count. Materials and methods: The performance measure for highest accuracy rate in novel vehicle detection using back propagation neural network (N=10 )over Oriented fast and rotated brief which identifies and counts distance. identification can be done using an image set to distinguish vehicles. The Gpower test used in about 85% (g power setting parameters: α=0.05 and power=0.85) Result: Back propagation neural network (94.32%) identifies vehicle and measures the count accurately over Oriented fast and rotated brief (91.16%) with a level of significance as 0.506 (Two tailed,p>0.05). Conclusion: The accuracy rate of Back propagation neural network is higher compared with that of oriented fast and rotated brief.