A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A Moving Objects Detection Algorithm Based on Improved Background Subtraction
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 03
An Integrated System for Moving Object Classification in Surveillance Videos
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Moving Object Segmentation Using Optical Flow and Depth Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
SSIAI '08 Proceedings of the 2008 IEEE Southwest Symposium on Image Analysis and Interpretation
Real time classification and tracking of multiple vehicles in highways
Pattern Recognition Letters
Detection and Segmentation of Moving Objects Based on Support Vector Machine
ISIP '10 Proceedings of the 2010 Third International Symposium on Information Processing
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This paper presents a new approach in recognition of moving objects captured by a surveillance camera. We have limited our area of study to the recognition of pedestrians and vehicles as it has ever increasing importance in the captured security surveillance as well as traffic monitoring systems. The primary phase of the method is the detection of moving objects using background subtraction and edge based subtraction. In the next phase, Speeded Up Robust Feature (SURF) of the moving object is extracted along with the height to width ratio. These features are used to correctly recognize the moving objects which then differentiate it to a pedestrians or vehicles. We have tested the performance of the system with sample videos as well as real time videos. The system shows a considerable recognition rate of 70% for pedestrians and 80% for vehicles. Statistical measures such as false discovery rate, recall and precision are used to measure the performance of the proposed system and 0.75% recall and 0.97% for precision has been obtained for pedestrians and vehicles.