Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm 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). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

SeamSeg: Video Object Segmentation using Patch Seams

In this paper, we propose a technique for video object segmentation using patch seams across frames. Typically, seams, which are connected paths of low energy, are utilised for retargeting, where the primary aim is to reduce the image size while preserving the salient image contents. Here, we adapt the formulation of seams for temporal label propagation. The energy function associated with the proposed video seams provides temporal linking of patches across frames, to accurately segment the object. The proposed energy function takes into account the similarity of patches along the seam, temporal consistency of motion and spatial coherency of seams. Label propagation is achieved with high fidelity in the critical boundary regions, utilising the proposed patch seams. To achieve this without additional overheads, we curtail the error propagation by formulating boundary regions as rough-sets. The proposed approach out-perform state-of-the-art supervised and unsupervised algorithms, on benchmark datasets.

Activity Recognition in Egocentric Life-Logging Videos

With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to life-logging, which is expected to be an important application for egocentric videos. In this work, we first conduct a comprehensive survey of existing egocentric video datasets. We observe that existing datasets do not emphasize activities relevant to the life-logging scenario. We build an egocentric video dataset dubbed LENA (Life-logging EgoceNtric Activities) (http://people.sutd.edu.sg/similar to 1000892/dataset) which includes egocentric videos of 13 fine-grained activity categories, recorded under diverse situations and environments using the Google Glass. Activities in LENA can also be grouped into 5 top-level categories to meet various needs and multiple demands for activities analysis research. We evaluate state-of-the-art activity recognition using LENA in detail and also analyze the performance of popular descriptors in egocentric activity recognition.

Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.

A survey on compressed domain video analysis techniques

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

No More HF: Teflon-Assisted Ultrafast Removal of Silica to Generate High-Surface-Area Mesostructured Carbon for Enhanced CO2 Capture and Supercapacitor Performance

An innovative technique to obtain high-surface-area mesostructured carbon (2545m(2)g(-1)) with significant microporosity uses Teflon as the silica template removal agent. This method not only shortens synthesis time by combining silica removal and carbonization in a single step, but also assists in ultrafast removal of the template (in 10min) with complete elimination of toxic HF usage. The obtained carbon material (JNC-1) displays excellent CO2 capture ability (ca. 26.2wt% at 0 degrees C under 0.88bar CO2 pressure), which is twice that of CMK-3 obtained by the HF etching method (13.0wt%). JNC-1 demonstrated higher H-2 adsorption capacity (2.8wt%) compared to CMK-3 (1.2wt%) at -196 degrees C under 1.0bar H-2 pressure. The bimodal pore architecture of JNC-1 led to superior supercapacitor performance, with a specific capacitance of 292Fg(-1) and 182Fg(-1) at a drain rate of 1Ag(-1) and 50Ag(-1), respectively, in 1m H2SO4 compared to CMK-3 and activated carbon.

No More HF: Teflon-Assisted Ultrafast Removal of Silica to Generate High-Surface-Area Mesostructured Carbon for Enhanced CO2 Capture and Supercapacitor Performance

An innovative technique to obtain high-surface-area mesostructured carbon (2545m(2)g(-1)) with significant microporosity uses Teflon as the silica template removal agent. This method not only shortens synthesis time by combining silica removal and carbonization in a single step, but also assists in ultrafast removal of the template (in 10min) with complete elimination of toxic HF usage. The obtained carbon material (JNC-1) displays excellent CO2 capture ability (ca. 26.2wt% at 0 degrees C under 0.88bar CO2 pressure), which is twice that of CMK-3 obtained by the HF etching method (13.0wt%). JNC-1 demonstrated higher H-2 adsorption capacity (2.8wt%) compared to CMK-3 (1.2wt%) at -196 degrees C under 1.0bar H-2 pressure. The bimodal pore architecture of JNC-1 led to superior supercapacitor performance, with a specific capacitance of 292Fg(-1) and 182Fg(-1) at a drain rate of 1Ag(-1) and 50Ag(-1), respectively, in 1m H2SO4 compared to CMK-3 and activated carbon.