IntelliBVR - Intelligent large-scale video retrieval for objects and events utilizing distributed deep-learning and semantic approaches

There is increasing reliance on the intelligent CCTV systems for effective analysis and interpretation of the streaming data to recognize activities and to ensure public safety. Monitoring videos captured by surveillance cameras is always a challenging and time-consuming task. There is a need for automated analysis using computer vision methods in order to extract spatial and temporal features to assist the authorities. Once videos are processed using computer vision technologies, another issue is how to index the extracted low-level features to search, analyze, and browse? How to bridge the semantic gap between the low-level features in Euclidean space and temporal relation across videos in a multi-stream environment? Similarly, how to deal with petascale video in the cloud while extracting the low-level and high-level features? In order to address such issues, in this paper, we propose a layered architecture for large-scale distributed intelligent video retrieval while exploiting deep-learning and semantic approaches called IntelliBVR. The base layer is responsible for large-scale video data curation. The second and the third layer is supposed to process and annotate videos, respectively while using deep learning on the top of distributed in-memory computing engine. Finally, the knowledge curation layer, where the extracted low-level and high-level features are mapped to the proposed ontology so that it can be searched and retrieved using semantic rich queries. Finally, we implement and show results, which project the effectiveness of IntelliBVR.