Modified Ant Colony Optimization for Human Recognition in Videos of Low Resolution

Privacy protections for people filmed in public settings is a prerequisite to widespread camera use. For this reason, low-resolution videos are used from which specific people can be reliably obscured. Since the human region in low-resolution videos comprises of so few pixels and so little information, human detection is more challenging there than it is in high-resolution videos. With the current state of affairs, one of the most important challenges is tracking a target from lower resolution movies. Identification or monitoring of persons in low-resolution movies has become a common issue in many domains due to a lack of appropriate data. This study presents a novel people-detection algorithm that makes use of low-resolution film to overcome the aforementioned problem. In the first stage, a three-step procedure is executed, the video data gathered from low-resolution videos from various form of data is considered. The captured video is separated into frames and transformed from RGB to gray-scale. Local Binary Pattern (LBP) method is used in the second phase to accomplish background subtraction. Thirdly the feature extraction is performed in which histogram of optical flow (HOF)and some of the features are extracted in the form of eigen values. Finally these features are optimized using Modified Ant Colony Optimization (MACO) model to remove the unwanted features and select global features. Finally, classification operation is performed using Support Vector Machine (SVM) classifier to recognize the person from lower resolution videos. The results obtained using the implemented MACO-SVM obtains good results when compared with existing techniques with rate of accuracy 91.46% for soccer dataset, 90.8% for KTH dataset and 89.75% accuracy using VIRAT dataset.