Activity monitoring of people in buildings using distributed smart cameras

Systems for monitoring the activity of people inside buildings (e.g., how many people are there, where are they, what are they doing, etc.) have numerous (potential) applications including domotics (control of lighting, heating, etc.), elderly-care (gathering statistics on the daily live) and video teleconferencing. We will discuss the key challenges and present the preliminary results of our ongoing research on the use of distributed smart cameras for activity monitoring of people in buildings. The emphasis of our research is on: - the use of smart cameras (embedded devices): video is processed locally (distributed algorithms), and only meta-data is send over the network (minimal data exchange) - camera collaboration: cameras with overlapping views work together in a network in order to increase the overall system performance - robustness: system should work in real conditions (e.g., robust to lighting changes) Our research setup consists of cameras connected to PCs (to simulate smart cameras), each connected to one central PC. The system builds in real-time an occupancy map of a room (indicating the positions of the people in the room) by fusing the information from the different cameras in a Dempster-Shafer framework

A low-cost visual sensor network for elderly care

A low-resolution visual sensor network enables monitoring of elderly people's health and safety at home, postponing institutionalized healthcare

PhD forum: multi-view occupancy maps using a network of low resolution visual sensors

An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e. g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter using the occupancy map per time instance as measurement. We found that for a room of 8.8 by 9.2 m, 4 cameras with a resolution as low as 64 by 48 pixels was sufficient to estimate accurate occupancy maps and track up to 4 people. These findings indicate that it is possible to use low resolution visual sensors to build a cheap, power efficient and privacy-friendly system for occupancy monitoring

Towards an ultra‐low‐power low‐cost wireless visual sensor node for fine‐grain detection of forest fires

Advances in electronics, sensor technologies, embedded hardware and software are boosting the application scenarios of wireless sensor networks. Specifically, the incorporation of visual capabilities into the nodes means a milestone, and a challenge, in terms of the amount of information sensed and processed by these networks. The scarcity of resources – power, processing and memory – imposes strong restrictions on the vision hardware and algorithms suitable for implementation at the nodes. Both, hardware and algorithms must be adapted to the particular characteristics of the targeted application. This permits to achieve the required performance at lower energy and computational cost. We have followed this approach when addressing the detection of forest fires by means of wireless visual sensor networks. From the development of a smoke detection algorithm down to the design of a low‐power smart imager, every step along the way has been influenced by the objective of reducing power consumption and computational resources as much as possible. Of course, reliability and robustness against false alarms have also been crucial requirements demanded by this specific application. All in all, we summarize in this paper our experience in this topic. In addition to a prototype vision system based on a full‐custom smart imager, we also report results from a vision system based on ultra‐low‐power low‐cost commercial imagers with a resolution of 30×30 pixels. Even for this small number of pixels, we have been able to detect smoke at around 100 meters away without false alarms. For such tiny images, smoke is simply a moving grey stain within a blurry scene, but it features a particular spatio‐temporal dynamics. As described in the manuscript, the key point to succeed with so low resolution thus falls on the adequate encoding of that dynamics at algorithm levelMinisterio de Economía y Competitividad TEC2012‐38921‐C02, IPT‐2011‐1625‐430000, IPC‐ 20111009 CDTIJunta de Andalucía TIC 2338‐201

A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images

Embedded Real Time Gesture Tracking

Video tracking is the process of locating a moving object (or several ones) in time using a camera. An algorithm evaluates the video frames and outputs the location of moving targets within the video frame

Human mobility monitoring in very low resolution visual sensor network

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics

Implementation of auto-rectification and depth estimation of stereo video in a real-time smart camera system

In this paper, we present a real-time, low power, and wireless embedded stereo vision system. The system consists of WiCa board (Wireless Camera board) and the methods developed using a smart camera processor, IC3D. IC3D is an SIMD video-analysis processor that has 320 processor elements. The proposed auto-rectification method is suitable for a parallel stereo system like WiCa. It is based on the matching of a planar background. After rectification, the two images of the background are coincident with each other, i.e., both the vertical and horizontal disparity of the background plane is removed. Then a dense matching method is implemented to achieve the depth map of the foreground object. Left-to-right checking and reliability checking is applied to reduce the error of the estimated depth. The system runs at 30fps and handles disparity up to 37 pixels in CIF (320x240 pixels) mode. 1

Real-Time Face Recognition On A Smart Camera

There is a rapidly growing demand for using cameras containing built-in intelligence for various purposes like surveillance and identification. Recently, face recognition is becoming an important application for these cameras. Face recognition requires lots of processing performance if real-time constraints are taken into account. The purpose of this paper is to demonstrate that by tuning the application algorithms, their implementation, and using a proper multi-processor architecture, face recognition can be performed real-time, up to 230 faces per second, using a smart camera not bigger than a typical surveillance camera