Video surveillance feeds run 24-7. The workers who monitor these feeds to pull salient information take hours on end to review this video, even at several times playback speed, often at greatly reduced accuracy. Our team knew there should be an intelligent way of analyzing these videos.

What it does

QuickLook automatically analyzes these video feeds, identifying salient points in the time series and flagging periods that may be of significance to investigators.

How we built it

We used open data video data sets such as Virat and Canadian Open Data. Our team took Faster RCNN Tensorflow models to recognize objects in our video data sets, and added heuristics to gather salient features from the analyzed videos.

Challenges we ran into

Scalability: Analyzing every frame in vast volumes of video data proved to be difficult. We bypassed this by utilizing a cluster of GPUs and parallelizing the work loads.

Processing and insight extraction: Processing the raw data to gather insights and recognize patterns/discrepancies within the video feeds.

Accomplishments that we're proud of

Data adaptation: Data gathering was made agnostic to the video and to the final purpose. The software we've created can be applied and (and further optimized) to perform in other settings, such as: convenience stores and school surveillance feeds.

Excellent UI/UX Design: The team created an end product with an interface designed for ease of use. By combining a simple UI with select important elements, we ensure that the 'assistive' aspect of our product delivers.

First-of-its-kind approach: QuickLook is a first-of-its-kind design that implements deep learning and Faster RCNN into activity and object detection for security feeds. By taking this approach, we hope to make progress in identifying key events, and further developing key strategies to optimize our processes (and even predict event occurences).

What's next for QuickLook

Our team is looking into deeper levels of object and activity recognition accuracy, and implementing a predictive event model to compliment our currrent video analytics product.

Built With

Share this project:


posted an update

Worked on the other side of the project, taking the data provided by Abhay manipulated it into a displayable set of graphs.

I am currently on my work placement, but quite interested in moving to Canada after my degree.

Thanks everyone, it was a really fun project! :D

Log in or sign up for Devpost to join the conversation.

posted an update

I worked on the Deep Learning and Computer Vision aspect of this project. I used the Faster-RCNN tensorflow model to analyse videos, look for salient points in video such as human activity level monitoring, unattended luggage detection etc. I handled the Python back end part which provides the statistics of the analysed video.

Log in or sign up for Devpost to join the conversation.