Occluded person Re-identification is a challenging task which aims to find or distinguish a specific person when the human body is occluded by obstacles, other persons or oneself. Some recent state-of-the-art works adopting a transformer and/or pose-guided methods have improved the feature representation and performances, but are still in trouble with both weak representation and heavy structure. In this paper, we suggest the novel methods of transformer-based Re-identification for the occluded person as follows. First, in data augmentation, instead of deleting an arbitrary area, only a part containing the keypoint features of a person is deleted for effective learning in occlusion. Second, we suggest a unique hierarchical patch and feature based cross attention combining the reliability-enhanced heatmaps and the output of the transformer intermediate layer, which can more effectively pay attention to the non-occluded human region. Third, we propose an entire-partial loss function with non-similarity grouping for more robust feature representation. We compare our approach and various existing methods for the mAP and Rank-1 performances on the Occluded-Duke, Occluded-ReID, Market-1501 and DukeMTMC datasets. Experimental results demonstrate that our proposed model not only outperforms the existing methods, but also has the smallest scale among state-of-the-art methods.

There is an increasing interest in deploying Internet protocol television (IPTV) over next-generation broadband networks which will allow services beyond basic triple-play functionalities. The IEEE 802.11 is considered today as the de-facto wireless access network for the last mile connections. Wireless IPTV can therefore become a reality in scenarios such as hot-spot, home, enterprise, airport, etc. The realm of wireless IPTV also brings forth challenges and new issues to overcome for enabling high quality delivery of time sensitive video content. Moreover, compressed video data are highly sensitive to wireless channel errors. In this paper, we present the promises and challenges of wireless IPTV.

Uncompressed HD (high-definition) video delivery over wireless personal area networks (WPANs) is a challenging problem because of the limited bandwidth and variations in channel. The 60 GHz millimeter-wave (mmWave) band has recently drawn much interest because of the huge bandwidth that it can provide from 57-66 GHz unlicensed spectrum available worldwide. However, to date a system design supporting uncompressed HD video over WPAN is still lacking. In this paper, we develop, simulate, and evaluate an mmWave system for supporting uncompressed video streaming over wireless (UVoW). New features of the UVoW system incorporates: (i) UEP (unequal error protection) where different video bits (MSBs and LSBs) are protected differently, (ii) a multi-CRC to determine whether MSB or/and LSB portions are in error, (iii) UV-ARQ, uncompressed video retransmission protocol which allows the receiver to request only those portions of a video packet which have high importance. Simulations indicate that the UVoW system achieves significantly higher video quality than normal systems under various wireless channel conditions. This shows that UVoW is a promising wireless system supporting uncompressed HD video.

In 60 GHz wireless networks, autonomous coordinator selection is required to find a device to coordinate the transmissions among devices. In order to minimize the power consumption for the coordinator, we utilize the direction information extracted from beamformed transmissions in finding the coordinator automatically. The problem is formulated as a K-center problem, which is a NP-hard problem in general. Analysis is carried out to find optimal solutions in certain tractable topologies. Numerical algorithms and simulation results are further presented for random two dimensional topologies.

Uncompressed HD (high-definition) video delivery over wireless personal area networks (WPANs) is a challenging problem because of the limited bandwidth and variations in channel. The most straight forward technique to recover from channel errors is to retransmit corrupted packets. However, retransmissions introduce significant delay/jitter and require additional bandwidth. Therefore, retransmissions may be unsuitable for uncompressed video streaming. In this paper, we develop, simulate, and evaluate an millimeter- wave (mmWave) system for supporting uncompressed video streams up to 3-Gbps without any retransmissions. New features of the mmWave system incorporates: (i) UEP (unequal error protection) where different video bits (MSBs and LSBs) are protected differently, (ii) a multiple-CRC to determine whether MSB or/and LSB portions are in error, (iii) RS code swapping (RSS), an error concealment scheme which can conceal some errors in video pixels. Simulations using real uncompressed HD images indicate that the proposed mmWave system can maintain good average PSNR (peak-signal-to-noise-ratio) under poor channel conditions, achieving what is generally accepted as a good picture quality with PSNR values greater than 40 dB. Moreover, the proposed system results in less fluctuating PSNR values.

The frequent and large-scale network attacks have led to an increased need for developing techniques for analyzing network traffic. This paper presents NetViewer, a network measurement approach that can simultaneously detect, identify, and visualize attacks and anomalous traffic in real-time by passively monitoring packet headers. We propose to represent samples of network packet header data as frames or images. With such a formulation, a series of samples can be seen as a sequence of frames or video, revealing certain kinds of attacks to the human eye. This enables techniques from image processing and video compression to be applied to the packet header data to reveal interesting properties of traffic. We show that "scene change analysis" can reveal sudden changes in traffic behavior or anomalies. We also show that "motion prediction" techniques can be employed to understand the patterns of some of the attacks. We show that it may be feasible to represent multiple pieces of data as different colors of an image enabling a uniform treatment of multidimensional packet header data. We compare the effectiveness of NetViewer with classical detection theory-based Neyman-Pearson test

The paper presents a network measurement approach to represent samples of network packet header data as frames or images. With such a formulation, a series of samples can be seen as a sequence of frames or video. This enables techniques from image processing and video compression to be applied to the analysis of packet header data to reveal interesting traffic properties. We show that traffic images can reveal sudden changes in traffic behavior or anomalies. Using a combination of visual modeling and trace-driven simulation, we evaluate how the design factors impact the representation of dynamic network traffic. In particular, we study the impact of sampling rate and retained DCT coefficients on the network traffic data representation.

In this paper, we present a network measurement mechanism that can detect and mitigate attacks and anomalous traffic in real-time using QoS regulation. The detection method rapidly pursues the dynamics of the network on the basis of correlation properties of the network protocols. By observing the proportion occupied by each traffic protocol and correlating it to that of previous states of traffic, it can be possible to determine whether the current traffic is behaving normally. When abnormalities are detected, our mechanism allows aggregated resource regulation of each protocol's traffic. The trace-driven results show that the rate-based regulation of traffic characterized by protocol classes is a feasible vehicle for mitigating the impact of network attacks on end servers.

This paper presents NetViewer, a network measurement approach that can simultaneously detect, identify and visualize attacks and anomalous traffic in real-time by passively monitoring packet headers. We propose to represent samples of network packet header data as frames or images. With such a formulation, a series of samples can be seen as a sequence of frames or video. This enables techniques from image processing and video compression to be applied to the packet header data to reveal interesting properties of traffic. We show that "scene change analysis" can reveal sudden changes in traffic behavior or anomalies. We also show that "motion prediction" techniques can be employed to understand the patterns of some of the attacks. We show that it may be feasible to represent multiple pieces of data as different colors of an image enabling a uniform treatment of multidimensional packet header data. We compare NetViewer with classical detection theory based Neyman-Pearson test and an IDS tool.