Surveillance Video for Mobile Devices

In this paper, we present a video encoding scheme that uses object-based adaptation to deliver surveillance video to mobile devices. The method relies on a set of complementary video adaptation strategies and generates content that matches various appliance and network resources. Prior to encoding, some of the adaptation strategies exploit video object segmentation and selective filtering in order to improve the perceived quality. Moreover, object segmentation enables the generation of automatic summaries and of simplified versions of the monitored scene. The performance of individual adaptation strategies is assessed using an objective video quality metric, which is also used to select the strategy that provides maximum value for the user under a given set of constraints. We demonstrate the effectiveness of the scheme on standard surveillance test sequences and realistic mobile client resource profiles

Scale Invariant Privacy Preserving Video via Wavelet Decomposition

Video surveillance has become ubiquitous in the modern world. Mobile devices, surveillance cameras, and IoT devices, all can record video that can violate our privacy. One proposed solution for this is privacy-preserving video, which removes identifying information from the video as it is produced. Several algorithms for this have been proposed, but all of them suffer from scale issues: in order to sufficiently anonymize near-camera objects, distant objects become unidentifiable. In this paper, we propose a scale-invariant method, based on wavelet decomposition

Design and control of a GYRO stabilized pan-tilt sensor system for mobile applications

Pan-tilt platforms are the motion control systems mostly used for controlled positioning of various devices (video cameras, sensors, antennas) in mobile applications such as aerial video surveillance systems, mobile robots for emergency situations, space rovers, defense modules and many others. In this project, a spherical parallel manipulator (SPM) platform is studied for designing a 3DOF system of pure rotation for optimal platform orientations suitable for mobile applications. An optimal trajectory generation and an orientation stabilization control systems will be designed using Model Predictive Control techniques and implemented on the platform prototype

Human action recognition with MPEG-7 descriptors and architectures

Modern video surveillance requires addressing high-level concepts such as humans' actions and activities. In addition, surveillance applications need to be portable over a variety of platforms, from servers to mobile devices. In this paper, we explore the potential of the MPEG-7 standard to provide interfaces, descriptors, and architectures for human action recognition from surveillance cameras. Two novel MPEG-7 descriptors, symbolic and feature-based, are presented alongside two different architectures, server-intensive and client-intensive. The descriptors and architectures are evaluated in the paper by way of a scenario analysis

ANDROID BASED SECURITY AND REMOTE SURVEILANCE SYSTEM

Mobile phones have been important Electronic devices in our life. Consequently, Home automation and security system becomes one of the prominent futures on mobile devices. In this paper, we have developed the android application that interfaces with the security system using wifi direct technology. The wifi technology is relatively new as compared to other technologies and there is huge potential of its growth and practical application. The android application loaded on mobile devices, can connect with security system and easy to use GUI. The application developed to command lock, unlock or video monitoring of the home. The security system then acts on these command and responds to the user. The CMOS camera and the motion detector are attached with security system for remote surveillance

Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV 2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser