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The extensive video surveillance networks gather an enormous amount of data exponentially on a daily basis and its management is a challenging task, requiring efficient and effective techniques for searching, indexing, and retrieval. The employed mainstream techniques are focusing on general category videos, where the important events in surveillance require fine-grained events retrieval. In this paper, we introduce an event-oriented feature selection mechanism by utilizing the intermediate convolutional layer of a pre-trained 3D-CNN model, that is selected after deep investigation of its weights and response to a particular event. The extracted exclusive features represent an event semantically and effectively eliminate those neurons which do not respond to an event. Furthermore, the event-oriented convolutional features are of very high-dimensions, requiring additional storage, and take more time in feature comparison for retrieval. Therefore, we generate compact binary codes from these features using principle component analysis (PCA) algorithm. This makes our system more efficient to retrieve videos from large scale database. We evaluated our approach on the challenging events of UCF101 and HMDB51 datasets for original features and generated compact codes to achieve reduced execution time and better precision and recall scores.

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval