Long range person and vehicle detection

Long range person and vehicle detection

van den Hoogen, B.
Uijens, W.
den Hollander, R.
Huizinga, W.
Dijk, J.
Schutte, K.

Dijk, J. (editor)

2020

Automatic detection and tracking of persons and vehicles can greatly increase situational awareness in many military applications. Various methods for detection and tracking have been proposed so far, both for rule-based and learning approaches. With the advent of deep learning, learning approaches generally outperform rule-based approaches. Pre-trained neural networks on datasets like MS COCO can give reasonable detection performance on military datasets. However, for optimal performance it is advised to optimize the training of these pre-trained networks with a representative dataset. In typical military settings, it is a challenge to acquire enough data, and to split the training and test set properly. In this paper we evaluate fine-tuning on military data and compare different pre- and post-processing methods. First we compare a standard pre-trained RetinaNet detector with a fine-tuned version, trained on similar objects, which are recorded at distances different than the distance in the test set. On the aspect of distance this train set is therefore out-of-distribution. Next, we augment the training examples by both increasing and decreasing their size. Once detected, we use a template tracker to follow the objects, compensating for any missing detections. We show the results on detection and tracking of persons and vehicles in visible imagery in a military long range detection setting. The results show the added value of fine-tuning a neural net with augmented examples, where final network performance is similar to human visual performance for detection of targets, with a target area of tens of pixels in a moderately cluttered land environment.

Augmentation
Person detection
RetinaNet
Tracking
Training neural nets
Vehicle detection
Deep learning
Learning systems
Military vehicles
Network security
Neural networks
Object tracking
Detection and tracking
Detection performance
Long range detections
Optimal performance
Postprocessing methods
Rule-based approach
Situational awareness
Trained neural networks
Military applications

http://resolver.tudelft.nl/uuid:8972c5f1-6819-4b03-b0ec-9074e559cd1d

955329

SPIE

9781510638990

0277-786X

Proceedings of SPIE - The International Society for Optical Engineering, Artificial Intelligence and Machine Learning in Defense Applications II 2020, 21 September 2020 through 25 September 2020

conference paper