Manabu Nishiyama

Stereo-based Pedestrian Detection using Multiple Patterns

Detecting pedestrians from a moving vehicle is a challenging problem since the essence of the task is to search non-rigid moving objects with various appearances in a dynamic and outdoor environment. In order to alleviate these difficulties, we propose a new human detection framework which makes the most use of stereo vision. While the conventional stereo-based detection methods initially generate regions of interest or ROIs on one of stereo images, the proposed one defines the ROIs on both left and right images. This paper presents two different ways for utilizing the stereo ROIs. Thefirst one is to classify the stereo ROIs individually and integrate the classification scores to obtain the final decision. The second one is to extend gradient-based local descriptors [1, 14] to multiple views and present new feature descriptors which we call Stereo HOG and Stereo CoHOG. Through experiments we show that both methods significantly reduce the false alarm rate while keeping the detection rate comparing with monocular-based methods.

18.2 A 1.9TOPS and 564GOPS/W heterogeneous multicore SoC with color-based object classification accelerator for image-recognition applications

Image recognition technologies have gained prominence in a variety of fields, such as automotive and surveillance, with dedicated image-recognition ICs being developed recently [1-2]. Image recognition ICs for an advanced driver assistance system (ADAS) have also been proposed [3]. However, future ADAS applications must support greater numbers of real-time recognition processes simultaneously, with higher detection rates and lower false-positive rates. For instance, adaptive cruise control (ACC), an application of ADAS, comprises many image recognition processes, such as pedestrian detection (PD), vehicle detection (VD), general obstacle detection (GOD), lane detection (LD), traffic light recognition (TLR), and traffic sign recognition (TSR). ACC also requires high detection accuracy to prevent unnecessary braking or acceleration. To satisfy these requirements, we have developed an SoC with two 4-core processor clusters and 14 hard-wired accelerators. It is designed to realize the six recognition processes (PD, VD, GOD, LD, TLR, and TSR) for ACC and automatic high beam (AHB) for headlight control. It achieves 1.9TOPS peak performance in 3.37W. This low power consumption enables the SoC to operate with passive cooling in a high-temperature automotive environment.

Temporal integration for on-board stereo-based pedestrian detection

Pedestrian detection is a difficult task for the following reasons. Firstly, pedestrians have many variations of size, pose, clothing, and motion. Secondly, the background tends to be crowded. Thirdly, many pedestrian-like patterns are observed in real environments. As a result, even if state-of-the-art pattern recognition method is used, a frame-by-frame detection has many false positives and negatives. This article presents a method of temporal integration for stereo-based pedestrian detection for improving the detection performance. In our method, a pedestrian is detected by evaluating consistency of the extracted pedestrian candidate for a short period of time. In order to get the consistency, the pedestrian candidate at each frame is combined with temporally corresponded ones through a hypothesis selection process. We demonstrate the effectiveness of our method by checking a recall and false positive number with 20,000 frames recorded in complex urban environments and public data sets. The proposed method reduces the number of false detection to one hundredth with holding recall.

Development of an attendant robot that builds a map by collaborating with a human

This paper describes an efficient indoor map building method that involves a robot collaborating with a human. A robot acquires the shape of a required part of an indoor environment, searches for a suitable picture from the scene of the destination, and registers it as a picture landmark at the same time as the robot follows the human. Once a picture landmark is registered on a map, a robot finds it and can self-localize from the next navigation. The validity of the proposed method is demonstrated by experiments carried out on an actual mobile robot “ApriAttenda™”, developed by Toshiba.