A Self-supervised Attention Mechanism

A Self-supervised Attention Mechanism To Help With Dense Optical Flow Estimation: Multi-object Tracking using self-supervised deep learning.

Multi-object Tracking using self-supervised deep learning

Before we get into what self-supervised attention means, let’s get an intuition of optical flow estimation and how it serves as an approach for tracking objects by both humans and computer vision systems.

It is a consensus that object tracking is a fundamental ability that is developed by a human baby at an early age of about two to three months. However, at the level of neurophysiology, the actual working mechanism of the human visual system still remains somewhat obscure. Similar to the human visual system, computer vision systems also widely use tracking for various applications like video surveillance and autonomous driving. The objective of a tracking algorithm is to relocate a particular set of objects in a given video sequence that it has identified in the initial frames. In the research literature related to tracking, it is studied under two major categories namely Visual Object Tracking (VOT) and Semisupervised Video Object Segmentation (Semi-VOS). The first one (VOT) aims to track objects by relocalizing object bounding boxes throughout the video sequence. Whereas the latter (Semi-VOS) tracks objects at a more fine-grained level through a pixel-level segmentation mask. In this blog, we will discuss the original idea behind the latter approach i.e Dense Optical Flow Estimation and how this kind of dense tracking approach is achieved through self-supervised attention mechanisms.

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