Out the Window: A Crowd-Sourced Dataset for Activity Classification in Security Video
This work addresses the need for better datasets in security video analysis, though it is incremental as it builds on existing benchmarks.
The researchers tackled the problem of activity classification in security video by introducing the Out the Window (OTW) dataset, which improved mean classification accuracy by 8.3% and boosted performance on challenging activities by 12.5%.
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.