Tomoyoshi Aizawa

Hand Waving Gesture Detection using a Far-infrared Sensor Array with Thermo-spatial Region of Interest

We propose a method of hand waving gesture detection using a far-infrared sensor array. The far-infrared sensor array captures the spatial distribution of temperature as a thermal image by detecting far-infrared waves emitted from heat sources. The advantage of the sensor is that it can capture human position and movement while protecting the privacy of the target individual. In addition, it works even at night-time without any light source. However, it is difficult to detect a gesture from a thermal image sequence captured by the sensor due to its low-resolution and noise. The problem is that the noise appears as a similar pattern as the gesture. Therefore, we introduce “Spatial Region of Interest (SRoI)” to focus on the region with motion. Also, to suppress the influence of other heat sources, we introduce “Thermal Region of Interest (TRoI)” to focus on the range of the human body temperature. In this paper, we demonstrate the effectiveness of the method through an experiment and discuss its result.

Human Tracking Using a Far-Infrared Sensor Array and a Thermo-Spatial Sensitive Histogram

We propose a human body tracking method using a far-infrared sensor array. A far-infrared sensor array captures the spatial distribution of temperature as a low-resolution image. Since it is difficult to identify a person from the low-resolution thermal image, we can avoid privacy issues. Therefore, it is expected to be applied for the analysis of human behaviors in various places. However, it is difficult to accurately track humans because of the lack of information sufficient to describe the feature of the target human body in the low-resolution thermal image. In order to solve this problem, we propose a thermo-spatial sensitive histogram suitable to represent the target in the low-resolution thermal image. Unlike the conventional histograms, in case of the thermo-spatial sensitive histogram, a voting value is weighted depending on the distance to the target’s position and the difference from the target’s temperature. This histogram allows the accurate tracking by representing the target with multiple histograms and reducing the influence of the background pixels. Based on this histogram, the proposed method tracks humans robustly to occlusions, pose variations, and background clutters. We demonstrate the effectiveness of the method through an experiment using various image sequences.

Deformed and Touched Characters Recognition

In this demonstration, we will show our Optical Character Recognition(OCR) technique. Character deformation and touching problems often occur during high-speed printing process in the machine vision industry. As a result, it is difficult for OCR system to segment and recognize characters properly. To solve these problems, we propose a novel OCR technique which is robust against deformation and touching. It splits regions of characters simply and excessively, recognizes all segments and merged regions, and obtains optimal segments using graph theory.

Action recognition from extremely low-resolution thermal image sequence

This paper proposes a Deep Learning-based action recognition method from an extremely low-resolution thermal image sequence. The method recognizes daily actions by humans (e.g. walking, sitting down, standing up, etc.) and abnormal actions (e.g. falling down) without privacy concerns. While privacy concerns can be ignored, it is difficult to compute feature points and to obtain a clear edge of the human body from an extremely low-resolution thermal image. To address these problems, this paper proposes a Deep Learning-based action recognition method that combines convolution layers and an LSTM layer for learning spatio-temporal representation, whose inputs are the thermal images and their frame differences cropped by the gravity center of human regions. The effectiveness of the proposed method was confirmed through experiments.

Robust estimation of less featured road surface for stereo-based vehicle detection

The conventional video vehicle detectors have some problems such as difficulty of parameter adjustment at initial settings that influences the detection performance, in addition to the performance degradation in adverse conditions. To solve these problems, stereo-based video vehicle detectors have been proposed. We propose a novel method to estimate relations automatically between a stereo camera and a road surface utilizing vehicles moving on the road.