Real-time thermal infrared moving target detection and recognition using deep learned features

Aparna Akula; Varinder Kaur; Neeraj Guleria; Ripul Ghosh; Satish Kumar

doi:10.1117/12.2559058

2 June 2020 Real-time thermal infrared moving target detection and recognition using deep learned features

Aparna Akula, Varinder Kaur, Neeraj Guleria, Ripul Ghosh, Satish Kumar

Proceedings Volume 11394, Automatic Target Recognition XXX; 1139415 (2020) https://doi.org/10.1117/12.2559058
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Surveillance applications demand round the clock monitoring of regions in constrained illumination conditions. Thermal infrared cameras which capture the heat emitted by the objects present in the scene appear as a suitable sensor technology for such applications. However, developing of AI techniques for automatic detection of targets for monitoring applications is challenging due to high variability of targets within a class, variations in pose of targets, widely varying environmental conditions, etc. This paper presents a real-time framework to detect and classify targets in a forest landscape. The system comprises of two main stages: the moving target detection and detected target classification. For the first stage, Mixture of Gaussians (MoG) background subtraction is used for detection of Region of Interest (ROI) from individual frames of the IR video sequence. For the second stage, a pre-trained Deep Convolutional Neural Network with additional custom layers has been used for the feature extraction and classification. A challenging thermal dataset created by using both experimentally generated thermal infrared images and from publically available FLIR Thermal Dataset. This dataset is used for training and validating the proposed deep learning framework. The model demonstrated a preliminary testing accuracy of 95%. The real-time deployment of the framework is done on embedded platform having an 8-core ARM v8.2 64-bit CPU and 512-core Volta GPU with Tensor Cores. The moving target detection and recognition framework achieved a frame rate of approximately 23 fps on this embedded computing platform, making it suitable for deployment in resource constrained environments.

Citation Download Citation

Aparna Akula, Varinder Kaur, Neeraj Guleria, Ripul Ghosh, and Satish Kumar "Real-time thermal infrared moving target detection and recognition using deep learned features", Proc. SPIE 11394, Automatic Target Recognition XXX, 1139415 (2 June 2020); https://doi.org/10.1117/12.2559058

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