Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video

TL;DR

The paper introduces the OTW dataset, a crowdsourced collection of outdoor activity videos captured from a novel scenario, which improves activity classification accuracy in security videos.

Contribution

It presents a new crowdsourced dataset with natural outdoor activities and demonstrates its effectiveness in enhancing classification performance.

Findings

8.3% improvement in mean classification accuracy

12.5% improvement on challenging activities involving people and vehicles

Dataset contains 5,668 instances of 17 activities

Abstract

The Out the Window (OTW) dataset is a crowdsourced activity dataset containing 5,668 instances of 17 activities from the NIST Activities in Extended Video (ActEV) challenge. These videos are crowdsourced from workers on the Amazon Mechanical Turk using a novel scenario acting strategy, which collects multiple instances of natural activities per scenario. Turkers are instructed to lean their mobile device against an upper story window overlooking an outdoor space, walk outside to perform a scenario involving people, vehicles and objects, and finally upload the video to us for annotation. Performance evaluation for activity classification on VIRAT Ground 2.0 shows that the OTW dataset provides an 8.3% improvement in mean classification accuracy, and a 12.5% improvement on the most challenging activities involving people with vehicles.