M
Manu Tom
Researcher at ETH Zurich
Publications - 25
Citations - 206
Manu Tom is an academic researcher from ETH Zurich. The author has contributed to research in topics: Climate change & Quantization (image processing). The author has an hindex of 8, co-authored 20 publications receiving 171 citations. Previous affiliations of Manu Tom include Indian Institute of Science & Swiss Federal Institute of Aquatic Science and Technology.
Papers
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Journal ArticleDOI
A survey on compressed domain video analysis techniques
TL;DR: This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards and includes only the analysis part, excluding the processing aspect of compressed domain.
Journal ArticleDOI
Lake ice monitoring with webcams
Muyan Xiao,Mathias Rothermel,Manu Tom,Silvano Galliani,Emmanuel P. Baltsavias,Konrad Schindler +5 more
TL;DR: In this article, a workflow for pixel-wise semantic segmentation of images into these classes, based on state-of-the-art encoder-decoder Convolutional Neural Networks (CNNs), is presented.
Journal ArticleDOI
Compressed domain human action recognition in H.264/AVC video streams
TL;DR: This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain that utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM).
Journal ArticleDOI
Lake Ice Detection in Low-Resolution Optical Satellite Images
TL;DR: In this article, a method for lake ice monitoring using low spatial resolution (250m-1000m) satellite images to determine whether a lake is frozen or not is described, where the most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with nonlinear support vector machine (SVM).
Proceedings ArticleDOI
Fast moving-object detection in H.264/AVC compressed domain for video surveillance
Manu Tom,R. Venkatesh Babu +1 more
TL;DR: This paper discusses a novel fast approach for moving object detection in H.264/AVC compressed domain for video surveillance applications that allows the video streams to be encoded with different quantization parameters across macroblocks thereby increasing flexibility in bit rate adjustment.