A real-time segmentation scheme for continuous color images

[[abstract]]A real-time segmentation scheme for continuous color images is presented in this paper. The proposed scheme consists of two main steps: (1) seed searching and region growing, (2) region-based change detection. A new color representation model, RBG-Ellipse, is proposed. This color model is similar to the HSI representation. However, the transformation between RGB and RGB-Ellipse is linear. Therefore, we are able to take advantage of noise tolerance processing as well as the efficiency in dealing with color difference computation. By using the proposed segmentation scheme, we implemented applications, (1) intelligent networked visual monitoring system and (2) user interface for distance learning to highlight the value of the proposed scheme. Users can view the results through our web site, http://www.can.tku.edu.tw.[[conferencetype]]國際[[conferencedate]]20010506~20010509[[booktype]]紙本[[conferencelocation]]Sydney, NSW, Australi

The Android Smartphone as an Inexpensive Sentry Ground Sensor

Proc. SPIE Conf. on Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV, Baltimore, MD, April 2012A key challenge of sentry and monitoring duties is detection of approaching people in areas of little human traffic. We are exploring smartphones as easily available, easily portable, and less expensive alternatives to traditional military sensors for this task, where the sensors are already integrated into the package. We developed an application program for the Android smartphone that uses its sensors to detect people passing nearby; it takes their pictures for subsequent transmission to a central monitoring station. We experimented with the microphone, light sensor, vibration sensor, proximity sensor, orientation sensor, and magnetic sensor of the Android. We got best results with the microphone (looking for footsteps) and light sensor (looking for abrupt changes in light), and sometimes good results with the vibration sensor. We ran a variety of tests with subjects walking at various distances from the phone under different environmental conditions to measure limits on acceptable detection. We got best results by combining average loudness over a 200 millisecond period with a brightness threshold adjusted to the background brightness, and we set our phones to trigger pictures no more than twice a second. Subjects needed to be within ten feet of the phone for reliable triggering, and some surfaces gave poorer results. We primarily tested using the Motorola Atrix 4G (Android 2.3.4) and HTC Evo 4G (Android 2.3.3) and found only a few differences in performance running the same program, which we attribute to differences in the hardware. We also tested two older Android phones that had problems with crashing when running our program. Our results provide good guidance for when and where to use this approach to inexpensive sensing

Detecting Abandoned Luggage Items in a Public Space

Visual surveillance is an important computer vision research problem. As more and more surveillance cameras appear around us, the demand for automatic methods for video analysis is increasing. Such methods have broad applications including surveillance for safety in public transportation, public areas, and in schools and hospitals. Automatic surveillance is also essential in the fight against terrorism. In this light, the PETS 2006 data corpus contains seven left-luggage scenarios with increasing scene complexity. The challenge is to automatically determine when pieces of luggage have been abandoned by their owners using video data, and set an alarm. In this paper, we present a solution to this problem using a two-tiered approach. The first step is to track objects in the scene using a trans-dimensional Markov Chain Monte Carlo tracking model suited for use in generic blob tracking tasks. The tracker uses a single camera view, and it does not differentiate between people and luggage. The problem of determining if a luggage item is left unattended is solved by analyzing the output of the tracking system in a detection process. Our model was evaluated over the entire data set, and successfully detected the left-luggage in all but one of the seven scenarios