This post is also available in: heעברית (Hebrew)

Deep Learning and Predictive Analytics are among the key technologies that will continue to benefit video surveillance solutions development and adoption in 2021.

These technologies advance video content analytics capabilities in several aspects: Deep Learning is a powerful Artificial Intelligence tool that is rapidly driving advancements in many areas. For video analytics specifically, Deep Learning is the catalyst for improved accuracy and expanded video scene understanding. For this reason, the more case-specific video data is available, the more competent the analytics for that end-user audience. However, surveillance scenarios available for training Deep Neural Networks are still somewhat limited, despite the increase in surveillance videos generated worldwide. According to a forecast by, the surveillance industry will make efforts to bridge this gap.

Deep Learning is expected to be applied in video content analytics in several missions: Anomaly detection will move from identifying simple anomalies to more intricate behavior detection. Anomaly detection will become a common tool for augmenting humans managing huge amounts of video.

The use of persistent surveillance powered by Deep Learning will become available, at small scales in the forms of cross-camera tracking and unified metadata across a site unlocking more value from existing and future video analytics infrastructure.

Advances in Predictive Analytics will also contribute to video analytics applications. Predictive analytics allow for better, more intricate information to be extracted from video. In the coming year, the development of more prediction-based capabilities that are derived directly from the video or from the rich video metadata generated will allow users to be more proactive and less reactive.