Dr. Ripul Ghosh Profile

Dr. Ripul Ghosh

Senior Scientist at CSIR - Central Scientific Instruments Organisation

SPIE Involvement:

Author

Area of Expertise:

Signal processing , Target Recognition , Machine Learning , Thermal Imaging

Publications (5)

Proceedings Article | 2 June 2020 Paper

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, 1139415 (2020) https://doi.org/10.1117/12.2559058

KEYWORDS: Target detection, Thermography, Target recognition, Infrared radiation, Infrared imaging, Feature extraction, Cameras, Infrared detectors, Infrared cameras, Video

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Proceedings Article | 2 June 2020 Presentation + Paper

Multi-feature optimization strategies for target classification using seismic and acoustic signatures

Ripul Ghosh, H. Sardana

Proceedings Volume 11394, 113940I (2020) https://doi.org/10.1117/12.2556457

KEYWORDS: Acoustics, Feature selection, Principal component analysis, Feature extraction, Statistical analysis, Unattended ground sensors, Target recognition, Seismic sensors, Machine learning, Surveillance

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Proceedings Article | 7 September 2018 Paper

A spatio-temporal deep learning approach for human action recognition in infrared videos

Anuj Shah, Ripul Ghosh, Aparna Akula

Proceedings Volume 10751, 1075111 (2018) https://doi.org/10.1117/12.2502993

KEYWORDS: Infrared imaging, Video, Thermography, Infrared radiation, Infrared cameras, Sensors, Convolutional neural networks, Machine learning

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Human action recognition in indoor environment can prove to be very crucial in avoiding serious accidents and (or) damage. Application domain spans from monitoring the actions of solitary elders or persons with disabilities to monitoring persons working alone in a chamber or in isolated industry environment. These scenarios demand an automatic near real-time activity recognition and alert to save life and assets. In this work, considering the fact that the sensing modality should be capable of working round the clock in a non-intrusive manner, we have opted for thermal infrared camera, which captures the heat emitted by objects in the scene and generates an image. Motivated by the recent success of convolutional neural networks (CNN) for human action recognition in IR images, we extend this work by incorporating one additional dimension i.e. the temporal information. In this work, we have designed and implemented a 3D-CNN for learning the spatial as well as the sequential features in the thermal IR videos. In this work, eight action classes are considered - Walking, Standing, Falling, Lying, Sitting, Falling from chair, Sitting up (recovering from fall from sitting posture), Getting up (recovering from fall from lying posture). To evaluate the proposed framework, infrared (IR) videos of different actions were generated in three diverse environments of home – inside study room, inside a bedroom and in the garden. The dataset comprised of 2641 and 894 IR videos for training and testing respectively, each of half a second duration performed by more than 50 volunteers. We have designed and implemented 3D-CNN, comprising of two blocks, each of two convolution and one max pool layer, which automatically constructs features from raw data incorporating both spatial and temporal information to learn actions. Network parameters are learned using back-propagation algorithm and the learning is supervised. Experimental results indicate 85% classification accuracy on 894 complex test videos of the proposed Spatio-Temporal Deep Learning architecture on the IR action dataset.

Proceedings Article | 7 September 2018 Paper

Towards an optimal bag-of-features representation for vehicle type classification in thermal infrared imagery

Aparna Akula, Ripul Ghosh, Neeraj Guleria, Satish Kumar, H. K. Sardana

Proceedings Volume 10751, 107511C (2018) https://doi.org/10.1117/12.2502154

KEYWORDS: Sensors, Infrared imaging, Infrared radiation, Image classification, Thermography, Infrared cameras, Cameras, Visualization, Infrared sensors, Classification systems

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Proceedings Article | 12 May 2016 Paper

Estimation of direction of arrival of a moving target using subspace based approaches

Ripul Ghosh, Utpal Das, Aparna Akula, Satish Kumar, H. Sardana

Proceedings Volume 9844, 98440R (2016) https://doi.org/10.1117/12.2223665

KEYWORDS: Acoustics, Data acquisition, Error analysis, Detection and tracking algorithms, Interference (communication), Fourier transforms, Signal to noise ratio, Sensors, Data processing, Chemical elements

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