An FPGA-based infant monitoring system

We have designed an automated visual surveillance system for monitoring sleeping infants. The low-level image processing is implemented on an embedded Xilinx’s Virtex II XC2v6000 FPGA and quantifies the level of scene activity using a specially designed background subtraction algorithm. We present our algorithm and show how we have optimised it for this platform

DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences

This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabilistic background model (DeepPBM) estimation approach is enabled by the power of deep neural networks in learning compressed representations of video frames and reconstructing them back to the original domain. We evaluated the performance of our DeepPBM in background subtraction on 9 surveillance videos from the background model challenge (BMC2012) dataset, and compared that with a standard subspace learning technique, robust principle component analysis (RPCA), which similarly estimates a deterministic low dimensional representation of the background in videos and is widely used for this application. Our method outperforms RPCA on BMC2012 dataset with 23% in average in F-measure score, emphasizing that background subtraction using the trained model can be done in more than 10 times faster

Feature-based object modelling for visual surveillance

This paper introduces a new feature-based technique for im-plicitly modelling objects in visual surveillance. Previous work has generally employed background subtraction and other image or motion based object segmentation schemes for the ſrst step in identifying objects worthy of attention. Given that background subtraction is a notoriously noisy pro-cess, this paper investigates an alternative strategy by instead employing feature (SIFT [1]) clustering to characterise ob-jects. The segmentation step is therefore performed on the sparse feature space instead of the image data itself. The paper also presents an application employing this idea for automatic detection of illegal dumping from CCTV footage. The Viterbi algorithm then allows robust tracking [2] of ob-jects generated from the spatial clustering of these sparse foreground feature maps. Index Terms — visual surveillance, SIFT, background modelling, foreground estimatio

An Enhanced Spatio-Temporal Human Detected Keyframe Extraction

Due to the immense availability of Closed-Circuit Television surveillance, it is quite difficult for crime investigation due to its huge storage and complex background. Content-based video retrieval is an excellent method to identify the best Keyframes from these surveillance videos. As the crime surveillance reports numerous action scenes, the existing keyframe extraction is not exemplary. At this point, the Spatio-temporal Histogram of Oriented Gradients - Support Vector Machine feature method with the combination of Background Subtraction is appended over the recovered crime video to highlight the human presence in surveillance frames. Additionally, the Visual Geometry Group trains these frames for the classification report of human-detected frames. These detected frames are processed to extract the keyframe by manipulating an inter-frame difference with its threshold value to favor the requisite human-detected keyframes. Thus, the experimental results of HOG-SVM illustrate a compression ratio of 98.54%, which is preferable to the proposed work\u27s compression ratio of 98.71%, which supports the criminal investigation