Summary: | 碩士 === 長庚大學 === 電機工程學系 === 102 === Vehicle detection is a common function in today’s ADAS ─ advanced
driver assistance systems. Vehicle detection plays an important role in alerting
the drivers on whether the environment in which they are driving is safe or not.
It collects information for each car near the host vehicle, and alerts the driver
whether each vehicle belongs to a highly risk category, or a low risk, safe and
normal car after a careful analysis the collected data. Usually we include
information like colors, distance, textures or shadows for analysis, but these
information are only available during the day time. Once the environment is
changed into the night time, the availability of these types of information
disappears because the lack of luminous beam. So we instead use lighted
vehicle lamps as information to detect vehicles in the night time.
In this thesis, we use the light source diffusion model to establish a diffuse
light intensity map, in which high intensity regions are taken as the locations of
the vehicle lamps. And we also use the optical flow algorithm to tracks the
lamps in real-time.
In summary, the following tasks have been accomplished in this thesis.
(1) Build a light intensity map based on the light source diffusion model.
(2) Analysis the light intensity map to find the distribution of the vehicle lamps.
(3) Using the center mass of ROI’s as the input to the optical flow algorithm to
distinguish the differences between them.
(4) Putting the collected data into a fuzzy system which analyses the possibility of the existence of vehicle by applying the fuzzy rules to the relationships
between the lamps.
The proposed method can detect vehicles by using information from vehicle
lamps. Also this method can decrease the interfering effects of non-vehicle light
sources. This method has been tested under real-time situations.
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