Benchmarking deep learning trackers on aerial videos

Breton Minnehan; Anthony Salmin; Karl Salva; Andreas Savakis

doi:10.1117/12.2323866

30 April 2018 Benchmarking deep learning trackers on aerial videos

Breton Minnehan, Anthony Salmin, Karl Salva, Andreas Savakis

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 1064915 (2018) https://doi.org/10.1117/12.2323866
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

In this paper, we benchmark five state-of-the-art trackers on aerial platform videos: Multi-domain Convolutional Neural Network (MDNET) tracker, which was the winner of the VOT2015 tracking challenge, the Fully Convolutional Neural network Tracker (FCNT), the Spatially Regularized Correlation Filter (SRDCF) tracker, the Continuous Convolution Operator Tracker (CCOT) tracker, which was the winner of the VOT2016 challenge, and the Tree structure Convolutional Neural Network (TCNN) tracker. We assess performance in terms of both tracking accuracy and processing speed based on two sets of videos: a subset of the OTB dataset where the cameras are located at a high vantage point and a new dataset of aerial videos captured by a moving platform. Our results indicate that these trackers performed as expected for the videos in the OTB subset, however, tracker performance degraded significantly in aerial videos due to target size, camera motion and target occlusions. The CCOT tracker yielded the best overall performance in terms of accuracy, while the SRDCF tracker was the fastest.

Citation Download Citation

Breton Minnehan, Anthony Salmin, Karl Salva, and Andreas Savakis "Benchmarking deep learning trackers on aerial videos", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064915 (30 April 2018); https://doi.org/10.1117/12.2323866

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