SIGMAP 2020 Abstracts

Area 1 - Multimedia Systems and Applications

Full Papers
Paper Nr: 5

SMSNet: A Novel Multi-scale Siamese Model for Person Re-Identification


Nirbhay K. Tagore and Pratik Chattopadhyay

Abstract: We propose a novel multi-scale Siamese architecture to perform person re-identification using deep learning. The scenario considered in this work is similar to that found in movie/concert halls, where persons enter in a queue one-by-one through the entry gates and leave in a similar way through the exit gates. Effectiveness of Siamese network based re-identification is evident from the recent research work in this domain. Here, we focus on improving the accuracy of the existing re-identification techniques by introducing different dilation rates in the convolution layers of the Siamese network, thereby enabling capturing of detailed visual features. We also introduce a silhouette part-based analysis to preserve the spatial relationships among the different silhouette segments at a high resolution. The proposed Siamese network model has been fine-tuned through cross-validation and the pre-trained network has been made available for further comparison. Rigorous evaluation of our approach against varying training parameters, as well as comparison with state-of-the-art methods over four popularly used data sets, namely, CUHK 01, CUHK 03, Market1501, and VIPeR, verify its effectiveness.

Short Papers
Paper Nr: 6

Quality of Experience of 360-degree Videos Played in Google Cardboard Devices


Saket V. Singh and Markus Fiedler

Abstract: Google Cardboard boxes provide a cost-efficient way to introduce users to Virtual Reality (VR) applications. These devices are suitable to be utilized for entertainment, gaming, and online studies. The 360-degree videos also known as immersive videos, play panoramic view in a video. The videos are played with a mobile phone mounted on a cardboard box and are viewed by wearing or holding the cardboard box. This paper studies the QoE of users (N=60) with QoE features user comfort, presence, and interactivity with panoramic video, based on QoE factors such as lens quality, weight and handling properties of the device. The experimental data is analysed in terms of statistical properties such as Mean Opinion Scores (MOS) including confidence intervals, as well as Percents of Good or Better (%GoB) and Poor or Worse (%PoW). Furthermore, the correlations between user ratings with respect to different groups of QoE features are investigated. Overall, the paper shows cardboard boxes to yield good-to-fair QoE for viewing panoramic videos.