Demo

Abstract

Online video chat services such as Chatroulette [1] and Omegle [2] that randomly match pairs of users in video chat sessions have become increasingly popular, with over twenty thousand online users at anytime during a day. A key problem encountered in such systems is the presence of misbehaving users (flashers) and obscene content. Our previous works [3] [4] prove that using some image recognition methods (skin-detection, dense SIFT) and machine learning algorithms could achieve significantly higher recall and better precision for flasher detection. Nowadays, with the rapid development of advanced mobile phones with both front and back cameras, we expect mobile video chat to become a popular extension of online video chat services. However, because of the computation-intensive features used by our previous solutions and mobile phones' hardware limitations such as memory size and CPU capacity, it is difficult to directly apply our previous works to mobile platforms. As smartphones are increasingly equipped with diverse sensing capabilities, we plan to utilize this multi-dimensional sensor information to extend flasher detection on mobile platform. This project explores how we can mine accelerometer and other mobile sensor data to infer some clues to optimize flasher detection accuracy while reducing the computation demands of flasher detection on the mobile device.In this demo, we will show our Android-based mobile video chat system (MVChat) in operation, which extends video chat to the mobile domain, beyond merely online services such as Chatroulette and Omegle. We will show how our previous flasher detection algorithm is capable of detecting misbehaving user video streams in a mobile environment. We will further demonstrate the capability of our system for real-time collection of multi-dimensional mobile sensor data such as acceleration, light, gyroscope, GPS as well as audio and video snapshots. Moreover, we will demonstrate the analysis of mobile sensor data analysis in order to identify key characteristics for detecting flashers on the mobile platform.Based on the Adobe AIR API, we establish our mobile video chat app by RTMFP (Real Time Media Flow Protocol) which is the same as the protocol used by most online video chat services such as Chatroulette and Omegle. It is thus possible to bind our solution with existing video chat services. Safety-permitting, we will show a demo of our client attached to an existing online video chat service.Figure 1 is captured while using our app in a mobile video chat session.