V. Srinivasa Somayazulu

On-Line Multi-View Video Summarization for Wireless Video Sensor Network

Battery lifetime is critical for wireless video sensors. To enable battery-powered wireless video sensors, low-power design is required. In this paper, we consider applying multi-view summarization to wireless video sensors to remove redundant contents such that the compression and transmission power can be reduced. A low-complexity online multi-view video summarization scheme is proposed. Experiments show that the proposed summarization method successfully reduces the video content while keeping important events. A power analysis of the system also shows that a significant amount of energy can be saved.

Distributed computing in IoT: System-on-a-chip for smart cameras as an example

There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover the context information and knowledge behind signals. To support such large-scale data size and computation tasks, it is not feasible to employ centralized solutions on cloud servers. Thanks for the advances of silicon technology, the cost of computation become lower, and it is possible to distribute computation on every node in IoT. In this paper, we take video sensing network as an example to show the idea of distributed computing in IoT. Existing related works are reviewed and the architecture of a system-on-a-chip solution for distributed smart cameras is proposed with coarse-grained reconfigurable image stream processing architecture. It can accelerate various computer vision algorithms for distributed smart cameras in IoT.

Distributed video coding: A promising solution for distributed wireless video sensors or not?

Low-power and low-cost distributed wireless video sensors play important roles for applications in machine-to-machine (M2M) and wireless sensor networks. Distributed video coding (DVC), an emerging coding technology based on Wyner-Ziv theory, seems to be a possible solution for implementing low-power video sensors since most of the computational complexity is moved from the encoder to the decoder. In this paper, existing works on DVC are discussed with rate-distortion and power consumption analyses compared with H.264/AVC-based approaches. We show that, since more transmission power is required for compensating the lower rate-distortion performance, the power consumption of sensor nodes using DVC is just similar to that of using H.264/AVC with zero motion vectors. Therefore, there is still a room for improvement to make DVC applicable for distributed wireless video sensors. Based on our analysis results, several possible research directions, such as studies on the tradeoff between hardware cost and system power consumption, are also addressed in this paper under a unified DVC framework.

LOW COMPLEXITY ON-LINE VIDEO SUMMARIZATION WITH GAUSSIAN MIXTURE MODEL BASED CLUSTERING

Techniques of video summarization have attracted significant research interests in the past decade due to the rapid progress in video recording, computation, and communication technologies. However, most of the existing methods analyze the video in an off-line manner, which greatly reduces the flexibility of the system. On-line summarization, which can progressively process video during video recording, is then proposed for a wide range of applications. In this paper, an on-line summarization method using Gaussian mixture model is proposed. As shown in the experiments, the proposed method outperforms other on-line methods in both summarization quality and computational efficiency. It can generate summarization with a shorter latency and much lower computation resource requirements.

Efficient cross-layer resource allocation for H.264/SVC video transmission over downlink of an LTE system

We formulate a PHY/APP cross-layer resource allocation problem for efficient transmission of video streams in a multiple-user LTE downlink system. To achieve a predetermined video PSNR, the number of bits needed for different users is calculated using a parameterized Rate-Distortion (RD) model proposed by [4]. We design an admission policy which assigns frequency-time OFDMA channel resource to video users based on a cross layer metric. For the admitted users, we use a tracking algorithm to serve the users as the video source content and channel conditions evolve. The goal of the resource allocation is to maximize the number of admitted users from a fixed user population and guarantee that the video quality of admitted users will achieve the predetermined PSNR threshold. The performance of the proposed algorithms is compared to four baseline algorithms which exploit different levels of cross layer knowledge about the channel and source characteristics, and the results show benefits from cross-layer optimization.

Power optimization of wireless video sensor nodes in M2M networks

Low-power wireless video sensor nodes play important roles for applications in machine-to-machine (M2M) network. Several design issues to optimize the power consumption of a video sensor node are addressed in this paper. For the video coding engine selection, the comparison between conventional video coding system and distributed video coding (DVC) system shows that although the rate-distortion performance of existing DVC codec still has room to improve, it can provide lower power consumption with a noisy transmission channel. Furthermore, it also demonstrated that video analysis unit can help to filter out video contents without event-of-interest to reduce transmission power. Finally, several future research directions are addressed, and the trade-off between the video analysis unit, video coding unit, and data transmission should be further studied to design wireless video sensors with optimized power consumption.

Wearable social camera: Egocentric video summarization for social interaction

Wearable social camera is an egocentric camera that summarizes the video of the users social activities. This paper proposes a core technology of the wearable social camera: egocentric video summarization for social interaction. Different from other works of third-person action/interaction recognition in egocentric videos, which focus on distinguishing different actions, this work finds the common features among all the interactions, which is called interaction features (IF). IF of the third-person is proposed to be composed of three parts: physical information of head, body languages, and emotional expression. Furthermore, hidden Markov model (HMM) is employed to model the interaction sequences, and a summarized video is generated with hidden Markov support vector machine (HM-SVM). Experimental results with a life-log dataset show that the proposed system performs well for summarizing life-log videos.

Bridge deep learning to the physical world: An efficient method to quantize network

As better performance is achieved by deep convolutional network with more and more layers, the increasing number of weighting and bias parameters makes it only possible to be implemented on servers in cyber space but infeasible to be deployed in physical-world embedded systems because of huge storage and memory bandwidth requirements. In this paper, we proposed an efficient method to quantize the model parameters. Instead of taking the quantization process as a negative effect on precision, we regarded it as a regularize problem to prevent overfitting, and a two-stage quantization technique including soft- and hard-quantization is developed. With the help of our quantization method, not only 93.75% of the parameter memory size can be reduced by replacing the word length from 32-bit to 2-bit, but the testing accuracy after quantization is also better than previous approaches in some dataset, and the additional training overhead is only 3% of the ordinary one.

Error resilience for key frames in distributed video coding with rate-distortion optimized mode decision

Distributed video coding (DVC) is a potential solution for distributed video sensors in wireless visual sensor and machine-to-machine (M2M) networks. However, the error resilience schemes have not been fully investigated in literatures. In this paper, we propose several error resilience schemes for key frame transmission of DVC, including resending, refining, and hybrid modes. The resending mode asks the transmitters to resend the lost packets as automatic repeat-request, the refining mode conceals the corrupted key frames by temporal error concealment techniques with considering the characteristics of DVC, and the hybrid mode can adaptively select the better mode packet-by-packet with the proposed rate-distortion optimized mode decision. Experimental results show that the proposed schemes can achieve around 6-dB gain in PSNR compared with a baseline approach with intra error concealment. Furthermore, the hybrid mode can achieve 0.5-dB gain in PSNR for some sequences compared with the better one among the resending and refining modes.

High-Rate UWB System Design Considerations

The ability to optimally exploit the 7.5 GHz of newly created nlicensed spectrum for UWB technology depends on addressing a number of challenging system design issues. This chapter provides an overview of many of these issues and some technical trade-offs and comparisons with different system designs. Some of the challenges include dealing with multipath propagation, energy capture, narrowband interference, rapid synchronization, and varying regulatory rules throughout the world, just to name a few.