Minimally Needed Evidence for Complex Event Recognition in Unconstrained Videos

This paper addresses the fundamental question -- How do humans recognize complex events in videos? Normally, humans view videos in a sequential manner. We hypothesize that humans can make high-level inference such as an event is present or not in a video, by looking at a very small number of frames not necessarily in a linear order. We attempt to verify this cognitive capability of humans and to discover the Minimally Needed Evidence (MNE) for each event. To this end, we introduce an online game based event quiz facilitating selection of minimal evidence required by humans to judge the presence or absence of a complex event in an open source video. Each video is divided into a set of temporally coherent microshots (1.5 secs in length) which are revealed only on player request. The player's task is to identify the positive and negative occurrences of the given target event with minimal number of requests to reveal evidence. Incentives are given to players for correct identification with the minimal number of requests. Our extensive human study using the game quiz validates our hypothesis - 55% of videos need only one microshot for correct human judgment and events of varying complexity require different amounts of evidence for human judgment. In addition, the proposed notion of MNE enables us to select discriminative features, drastically improving speed and accuracy of a video retrieval system.