A method for detecting an object's motion in images that suffer from camera shake or images with camera egomotion
is proposed. This approach is based on edge orientation codes and on the entropy calculated from a histogram of the edge
orientation codes. Here, entropy is extended to spatio-temporal entropy. We consider that the spatio-temporal entropy
calculated from time-series orientation codes can represent motion complexity, e.g., the motion of a pedestrian. Our
method can reject false positives caused by camera shake or background motion. Before the motion filtering, object
candidates are detected by a frame-subtraction-based method. After the filtering, over-detected candidates are evaluated
using the spatio-temporal entropy, and false positives are then rejected by a threshold. This method could reject 79 to 96
[%] of all false positives in road roller and escalator scenes. The motion filtering decreased the detection rate somewhat
because of motion coherency or small apparent motion of a target. In such cases, we need to introduce a tracking method
such as Particle Filter or Mean Shift Tracker. The running speed of our method is 32 to 46 ms per frame with a 160×120
pixel image on an Intel Pentium 4 CPU at 2.8 GHz. We think that this is fast enough for real-time detection. In addition,
our method can be used as pre-processing for classifiers based on support vector machines or Boosting.
We develop a rapid object-candidates detector using Increment Sign Correlation (ISC). Our method aims to detect
candidates of objects such as people or vehicles in real time using ISC and a simple shape model. Our method is similar
to Generalized Hough Transform (GHT). However we modify its voting process. We use ISC for detecting object
candidates instead of the shape voting done by GHT. ISC is robust against shading and low image contrast due to
lighting changes because Increment Sign (IS) is insensitive to a perturbation of direction of intensity gradient. The
computational cost of IS is lower than that of the gradient also. From the results of our experiment, our detector can run
with a 320×240 pixel image within 32 milliseconds on a Pentium 4 processor at 2.8 GHz. Given the initial template size
of 10×20 pixels, the number of candidates decreases from 170,196 sub-windows in a 320×240 pixel image to 400 at
most with the miss rate of 0.2 %. The detection rate is enough for more precise detectors which need to use richer image
features. The experimental results using real image sequences are reported.
A method of real-time object detection for video surveillance systems has been developed. The method aims to realize robust object detection by using Radial Reach Correlation (RRC). We also apply a statistical background estimation to cope with dynamic and complex environments. The computational cost of RRC is higher than the simple subtraction method and the background estimation method based on statistical approach needs large memory. It is necessary to reduce the calculation cost in order to apply to an embedded image processing device. Our method is composed of two techniques: fast RRC algorithm and background estimation based on statistical approach with cumulative averaging process. As a result, without deterioration in detection accuracy, the processing time of object detection can be decreased to about 1/4 in comparison with normal RRC.
Recently, developing of image processing method which enables to track to moving objects on time series images
taken by a fixed camera is one of important subjects in the field of machine vision. Here, we try to consider
influences by change in brightness and change of region caused by moving objects, respectively. In this paper,
we introduce a new tracking method which can be reduced the influences by those changes. First, we use Radial
Reach Filter in order to detect the moving objects. In addition, the moving objects can be tracked by an image
processing based on information obtained by applying RRF and block division. Further, we propose a method in
the case that changes size of moving object by time progress. Finally, through experiments we show the validity
of our proposed method.
In this paper, we propose a new method of object detection. In the past, there are various methods of object detection. Especially, the method of the background subtraction has the effectiveness. However, the methods based on brightness differences are easily influenced by change in lighting condition. In this paper, we use Radial Reach Filter (RRF). RRF is called as the effective method of the change in lighting conditions. However, RRF is not considered change that caused by moving objects on the background image. Then, we propose the new method of object detection that considered motion of the moving objects on the background image. And, we verify the effectiveness by the experiments using a time series image.
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