This paper presents a stereo matching approach for a novel multi-perspective panoramic stereo vision system, making use of asynchronous and non-simultaneous stereo imaging towards real-time 3D 360° vision. The method is designed for events representing the scenes visual contrast as a sparse visual code allowing the stereo reconstruction of high resolution panoramic views. We propose a novel cost measure for the stereo matching, which makes use of a similarity measure based on event distributions. Thus, the robustness to variations in event occurrences was increased. An evaluation of the proposed stereo method is presented using distance estimation of panoramic stereo views and ground truth data. Furthermore, our approach is compared to standard stereo methods applied on event-data. Results show that we obtain 3D reconstructions of 1024 × 3600 round views and outperform depth reconstruction accuracy of state-of-the-art methods on event data.

This paper presents a recently developed dynamic stereo vision sensor system and its application for fall detection towards safety for elderly at home. The system consists of (1) two optical detector chips with 304×240 event-driven pixels which are only sensitive to relative light intensity changes, (2) an FPGA for interfacing the detectors, early data processing, and stereo matching for depth map reconstruction, (3) a digital signal processor for interpreting the sensor data in real-time for fall recognition, and (4) a wireless communication module for instantly alerting caring institutions. This system was designed for incident detection in private homes of elderly to foster safety and security. The two main advantages of the system, compared to existing wearable systems are from the application's point of view: (a) the stationary installation has a better acceptance for independent living comparing to permanent wearing devices, and (b) the privacy of the system is systematically ensured since the vision detector does not produce real images such as classic video sensors. The system can actually process about 300 kevents per second. It was evaluated using 500 fall cases acquired with a stuntman. More than 90% positive detections were reported. We will show a live demonstration during ISCAS2012 of the sensor system and its capabilities.

This live demonstration aims to show a real-time people tracking using an embedded smart stereo vision system (3DVS) consisting of two asynchronous (biologically-inspired) dynamic vision sensors and a processing unit (DSP) embedding a detection and tracking algorithm.

Biologically-inspired dynamic vision sensors have been introduced in 2002 which asynchronously detect the significant relative light intensity changes in a scene and output them in a form of Address-Event representation. These vision sensors capture dynamical discontinuities on-chip for a reduced data volume compared to that from intensity images. Therefore, they support detection, segmentation and tracking of moving objects in the Address-Event space by exploiting the generated events, as a reaction to intensity changes, resulting from the scene dynamics. Object tracking has been previously demonstrated and reported in scientific publications using monocular dynamic vision sensors. This paper contributes with presenting and demonstrating a tracking algorithm using the 3D sensing technology based on the stereo dynamic vision sensor. This system is capable of detecting and tracking persons within a 4m range at an effective refresh rate of the depth map of up to 200 per second. The 3D system is evaluated for people tracking and the tests showed that up to 60k Address-Events/s can be processed for real-time tracking.