Digital video fingerprinting using Machine Learning

Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.

With the advent of machine learning techniques, the accuracy for object detection has increased drastically.

A major challenge in many of the object detection systems is the dependency on other computer vision techniques for helping the machine learning based approach, which leads to slow and non-optimal performance.

The project aims to incorporate state-of-the-art technique for object detection with the goal of achieving high accuracy at faster rate.

Compared to other region proposal classification networks (fast RCNN) which perform detection on various region proposals and thus end up performing prediction multiple times for various regions in a image, Yolo architecture is more like FCNN (fully convolutional neural network) and passes the image (nxn) once through the FCNN and output is (mxm) prediction.

This model has several advantages over classifier-based systems. It looks at the whole image at test time so its predictions are informed by global context in the image. It also makes predictions with a single network evaluation unlike systems like R-CNN which require thousands for a single image. This makes it extremely fast, more than 1000x faster than R-CNN and 100x faster than Fast R-CNN.

Successfully identified objects in the frames extracted from the video and timestamp is bound to the objects .

We found that DNN architectures are capable of learning powerful features from weakly-labelled data that far surpass feature-based methods in performance .