Haruki Furusawa

Methods of traffic flow measurement using spatio-temporal image

Traffic flow measurement sensors, which measure vehicle velocity and detect traffic accidents, traffic congestion and so on using image processing, have been installed. For the sensors we utilized spatio-temporal images, which are used to process sequential images; as a recognition method for vehicles, we developed Directional-Temporal plane Transform (DTT) which transforms spatio-temporal image to 2-D data onto a directional-temporal plane. The rear-end-collision prevention system using this method decreased accidents to 1/3 as compared to the last few years before installation at a place on a national highway where many rear-end collisions had occurred. However, in the case where cameras are set up at low positions such as in tunnels, it is difficult to measure vehicle velocity in heavy traffic conditions because one vehicle often blocks the view of another in the images. To overcome this drawback, we propose a method for measurement of velocity for a group of vehicles by way of measuring moving time of the group by matching patterns, of DTT data at several points. We describe DTT-including its application-and the method for measurement of velocity for a group of vehicles, and then show the experimental results using road images.

A method of motion analysis using spatiotemporal image-directional temporal plane transform

Some of the recent image processing methods proposed for detecting object moving in 3-D space form a spatiotemporal image by placing consecutive camera images side by side in temporal order. This paper presents one such method, the directional temporal plane transform method (DTT), an image processing method that is not influenced by environmental changes. Assuming that the objects are moving on a nearly constant path, DTT first extracts significant data about these objects from each image, and then makes a 1-D data stream by projecting the data long a directional axis parallel to the moving loci. After that, by placing the 1-D data from individual frames side by side in temporal order, the spatiotemporal image is transformed into a 2-D image on a directional temporal plane. Since the object motion is represented by regions of this 2-D image, moving objects can be detected by simple 2-D image processing. This method was used on an experimental basis to detect vehicles running along a road. Experimental results show the effectiveness of the method. An advantage of this method is its ability to detect vehicles using the same processing during both daytime and nighttime-a feature that has not been achieved with conventional methods.

Recognition of moving vehicles using directional-temporal plane transform

Traffic flow measurement systems, which recognize vehicles and measure traffic parameters by image processing, are researched. We propose a new recognition method for moving vehicles using directional-temporal plane transform (DTT) for the system. DTT is a transformation from spatiotemporal data to 2-D data onto a directional-temporal plane. The transformation is done by projecting feature data of vehicles to a directional axis which is roughly parallel to the moving loci, and by placing the projected data stream side by side in temporal order. We also show an effective extraction algorithm of vehicles from the 2-D data obtained by DTT. Experimental results using real images demonstrate the effectiveness of this method.