arXiv subjects

Subhasis Chaudhuri

Publications and source records attributed to Subhasis Chaudhuri.

41 records · Page 3Linked to original sources

arXiv2018

On QoS-Compliant Telehaptic Communication over Shared Networks

The development of communication protocols for teleoperation with force feedback (generally known as telehaptics) has gained widespread interest over the past decade. Several protocols have been proposed for performing telehaptic interaction over shared networks. However, a comprehensive analysis of the impact of network cross-traffic on telehaptic streams, and the feasibility of Quality of Service (QoS) compliance is lacking in the literature. In this paper, we seek to fill this gap. Specifically, we explore the QoS experienced by two classes of telehaptic protocols on shared networks - Constant Bitrate (CBR) protocols and adaptive sampling based protocols, accounting for CBR as well as TCP cross-traffic. Our treatment of CBR-based telehaptic protocols is based on a micro-analysis of the interplay between TCP and CBR flows on a shared bottleneck link, which is broadly applicable for performance evaluation of CBR-based media streaming applications. Based on our analytical characterization of telehaptic QoS, and via extensive simulations and real network experiments, we formulate a set of sufficient conditions for telehaptic QoS-compliance. These conditions provide guidelines for designers of telehaptic protocols, and for network administrators to configure their networks for guaranteeing QoS-compliant telehaptic communication.

cs.NI↗

arXiv2017

Spatio-temporal interaction model for crowd video analysis

We present an unsupervised approach to analyze crowd at various levels of granularity $-$ individual, group and collective. We also propose a motion model to represent the collective motion of the crowd. The model captures the spatio-temporal interaction pattern of the crowd from the trajectory data captured over a time period. Furthermore, we also propose an effective group detection algorithm that utilizes the eigenvectors of the interaction matrix of the model. We also show that the eigenvalues of the interaction matrix characterize various group activities such as being stationary, walking, splitting and approaching. The algorithm is also extended trivially to recognize individual activity. Finally, we discover the overall crowd behavior by classifying a crowd video in one of the eight categories. Since the crowd behavior is determined by its constituent groups, we demonstrate the usefulness of group level features during classification. Extensive experimentation on various datasets demonstrates a superlative performance of our algorithms over the state-of-the-art methods.

cs.CV↗

arXiv2017

An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition

We present an integrated framework for simultaneous tracking, group detection and multi-level activity recognition in crowd videos. Instead of solving these problems independently and sequentially, we solve them together in a unified framework to utilize the strong correlation that exists among individual motion, groups, and activities. We explore the hierarchical structure hidden in the video that connects individuals over time to produce tracks, connects individuals to form groups and also connects groups together to form a crowd. We show that estimation of this hidden structure corresponds to track association and group detection. We estimate this hidden structure under a linear programming formulation. The obtained graphical representation is further explored to recognize the node values that corresponds to multi-level activity recognition. This problem is solved under a structured SVM framework. The results on publicly available dataset show very competitive performance at all levels of granularity with the state-of-the-art batch processing methods despite the proposed technique being an online (causal) one.

cs.CV↗

arXiv2017

Congestion Control for Network-Aware Telehaptic Communication

Telehaptic applications involve delay-sensitive multimedia communication between remote locations with distinct Quality of Service (QoS) requirements for different media components. These QoS constraints pose a variety of challenges, especially when the communication occurs over a shared network, with unknown and time-varying cross-traffic. In this work, we propose a transport layer congestion control protocol for telehaptic applications operating over shared networks, termed as dynamic packetization module (DPM). DPM is a lossless, network-aware protocol which tunes the telehaptic packetization rate based on the level of congestion in the network. To monitor the network congestion, we devise a novel network feedback module, which communicates the end-to-end delays encountered by the telehaptic packets to the respective transmitters with negligible overhead. Via extensive simulations, we show that DPM meets the QoS requirements of telehaptic applications over a wide range of network cross-traffic conditions. We also report qualitative results of a real-time telepottery experiment with several human subjects, which reveal that DPM preserves the quality of telehaptic activity even under heavily congested network scenarios. Finally, we compare the performance of DPM with several previously proposed telehaptic communication protocols and demonstrate that DPM outperforms these protocols.

cs.NI↗

arXiv2011

Estimation of the Embedding Capacity in Pixel-pair based Watermarking Schemes

Estimation of the Embedding capacity is an important problem specifically in reversible multi-pass watermarking and is required for analysis before any image can be watermarked. In this paper, we propose an efficient method for estimating the embedding capacity of a given cover image under multi-pass embedding, without actually embedding the watermark. We demonstrate this for a class of reversible watermarking schemes which operate on a disjoint group of pixels, specifically for pixel pairs. The proposed algorithm iteratively updates the co-occurrence matrix at every stage, to estimate the multi-pass embedding capacity, and is much more efficient vis-a-vis actual watermarking. We also suggest an extremely efficient, pre-computable tree based implementation which is conceptually similar to the co-occurrence based method, but provides the estimates in a single iteration, requiring a complexity akin to that of single pass capacity estimation. We also provide bounds on the embedding capacity. We finally show how our method can be easily used on a number of watermarking algorithms and specifically evaluate the performance of our algorithms on the benchmark watermarking schemes of Tian [11] and Coltuc [6].

cs.CR↗