Report on UG^2+ Challenge Track 1: Assessing Algorithms to Improve Video Object Detection and Classification from Unconstrained Mobility Platforms

TL;DR

This paper reports on the UG^2+ Challenge Track 1, evaluating algorithms that enhance video frames from UAVs to improve object detection and classification under challenging conditions.

Contribution

It provides a comprehensive analysis of various image restoration and enhancement algorithms combined with machine learning for UAV video recognition tasks.

Findings

Certain image pre-processing algorithms significantly improved detection accuracy.

Enhanced frames led to better classification performance in challenging scenarios.

Analysis identified the most effective algorithm combinations for UAV video recognition.

Abstract

How can we effectively engineer a computer vision system that is able to interpret videos from unconstrained mobility platforms like UAVs? One promising option is to make use of image restoration and enhancement algorithms from the area of computational photography to improve the quality of the underlying frames in a way that also improves automatic visual recognition. Along these lines, exploratory work is needed to find out which image pre-processing algorithms, in combination with the strongest features and supervised machine learning approaches, are good candidates for difficult scenarios like motion blur, weather, and mis-focus -- all common artifacts in UAV acquired images. This paper summarizes the protocols and results of Track 1 of the UG^2+ Challenge held in conjunction with IEEE/CVF CVPR 2019. The challenge looked at two separate problems: (1) object detection improvement in video, and (2) object classification improvement in video. The challenge made use of the UG^2 (UAV, Glider, Ground) dataset, which is an established benchmark for assessing the interplay between image restoration and enhancement and visual recognition. 16 algorithms were submitted by academic and corporate teams, and a detailed analysis of how they performed on each challenge problem is reported here.