Proc. SPIE. 7724, Real-Time Image and Video Processing 2010
KEYWORDS: Target detection, Image processing algorithms and systems, Signal to noise ratio, Detection and tracking algorithms, Image segmentation, Image processing, Digital filtering, Image filtering, Target recognition, Signal detection
For target detection within a large-field cluttered background from a long distance, several difficulties, involving low
contrast between target and background, little occupancy, illumination ununiformity caused by vignetting of lens, and
system noise, make it a challenging problem. The existing approaches to dim target detection can be roughly divided into
two categories: detection before tracking (DBT) and tracking before detection (TBD). The DBT-based scheme has been
widely used in practical applications due to its simplicity, but it often requires working in the situation with a higher
signal-to-noise ratio (SNR). In contrast, the TBD-based methods can provide impressive detection results even in the
cases of very low SNR; unfortunately, the large memory requirement and high computational load prevents these
methods from real-time tasks. In this paper, we propose a new method for dim target detection. We address this problem
by combining the advantages of the DBT-based scheme in computational efficiency and of the TBD-based in detection
capability. Our method first predicts the local background, and then employs the energy accumulation and median filter
to remove background clutter. The dim target is finally located by double window filtering together with an improved
high order correlation which speeds up the convergence. The proposed method is implemented on a hardware platform
and performs suitably in outside experiments.
Real-time target detection against strong (bright) background under daytime is a challenging and leading edge subject, and also is a key technique for imaging tracking system. Strong background makes CCD image sensor work in critical saturation state, and imaging target contrast is very low. It's very difficult to accurately and stably track due to the complex characteristics of imaging target, such as strong clutter background, low contrast, and low signal to noise radio (SNR). So the key techniques for detecting and tracking target are eliminating the disturbance of diffuse reflection and beacon, synchronous detection, improving the performance of real-time image processing with high frame rate and high sampling rate.
A robust strategy for detecting and tracking day-time target was proposed in this paper. A series of efficient approaches ware presented to improve performance of detection and tracking in precision and stability, including strong background and noise suppression, image enhancement, adaptive thresholding, region merging based on morphology, recognition and tracking algorithm and so on.
In the end, we summarized and built the effictive flow for detecting and tracking target against strong background under daytime. The results of combining computer simulation with practical detection experiments show that the above-mentioned approaches are feasible and significant for real-time tracking system.