Reliable object recognition system for cloud video data based on LDP features

摘要

Object recognition is one of the research areas with good scope in most of the applications. However, the object recognition on cloud stored data is very limited and the video based object recognition systems are minimal. Taking this into account, the videos are processed for recognizing the objects of interest by incorporating advanced image processing activities. The video frames are extracted from the videos for recognizing the objects. In order to recognize the objects, the objects have to be detected first. The objects are detected by means of SURF detector and the combination of local and global LDP features is extracted. Finally, the objects present in the videos are matched with the objects of interest. The performance of the proposed object recognition system for cloud video data is tested in three rounds. Initially, the proposed work is tested with different videos and then the proposed work is evaluated by varying the feature extractors such as Local Binary Pattern (LBP), Local LDP, Global LDP. Finally, the video processing time is calculated in terms of both CPU and GPU. All the performance evaluations are carried out in terms of accuracy, sensitivity, specificity and time consumption. The performance of the proposed approach is proven to be satisfactory.