Abstract: An image adaptive feature extraction method includes dividing an image into a plurality of blocks, performing a feature extraction processing on the plurality of blocks, and obtaining a block feature from each of the plurality of blocks after the feature extraction processing; calculating each block feature by means of a support vector machine (SVM) classifier, wherein each block feature is calculated to obtain a hyperplane normal vector; setting a threshold value, determining the block feature according to the hyperplane normal vector, recording the block as an adaptive feature block when a value of the hyperplane normal vector is higher than the threshold value, and integrating each adaptive feature block to form an adaptive feature image. Because an image adaptive feature extraction process is performed before a pedestrian image detection is calculated, and effective feature data is then selected, computational efficiency is boosted and detection pedestrian error probability is reduced.

Abstract: An image adaptive feature extraction method includes dividing an image into a plurality of blocks, performing a feature extraction processing on the plurality of blocks, and obtaining a block feature from each of the plurality of blocks after the feature extraction processing; calculating each block feature by means of a support vector machine (SVM) classifier, wherein each block feature is calculated to obtain a hyperplane normal vector; setting a threshold value, determining the block feature according to the hyperplane normal vector, recording the block as an adaptive feature block when a value of the hyperplane normal vector is higher than the threshold value, and integrating each adaptive feature block to form an adaptive feature image. Because an image adaptive feature extraction process is performed before a pedestrian image detection is calculated, and effective feature data is then selected, computational efficiency is boosted and detection pedestrian error probability is reduced.

Abstract: A method for extracting features of a thermal image is provided. The method includes: reading a thermal image, and dividing the thermal image into a plurality of block images; and extracting a histogram of oriented gradient (HOG) feature histogram from each of the plurality of block images, and transforming the HOG feature histogram of each of the plurality of block images into a symmetric weighting HOG (SW-HOG) feature histogram. The SW-HOG feature histogram is obtained by multiplying a histogram of gradient intensity distribution by a block weighting. The method increases weightings of blocks which cover human contours and reduces weightings of blocks of an internal region of a human appearance through analyzing thermal lightness difference of regions within blocks, to reduce the influence of clothes in the internal region and the influence of the background region.

Abstract: A method for extracting features of a thermal image is provided. The method includes: reading a thermal image, and dividing the thermal image into a plurality of block images; and extracting a histogram of oriented gradient (HOG) feature histogram from each of the plurality of block images, and transforming the HOG feature histogram of each of the plurality of block images into a symmetric weighting HOG (SW-HOG) feature histogram. The SW-HOG feature histogram is obtained by multiplying a histogram of gradient intensity distribution by a block weighting. The method increases weightings of blocks which cover human contours and reduces weightings of blocks of an internal region of a human appearance through analyzing thermal lightness difference of regions within blocks, to reduce the influence of clothes in the internal region and the influence of the background region.

Abstract: An all-weather thermal-image pedestrian detection method includes (a) capturing diurnal thermal images and nocturnal thermal images of a same pedestrian and non-pedestrian object in a same defined block to create a sample database of thermal images, wherein the sample database comprises pedestrian samples and non-pedestrian samples; (b) performing LBP encoding on the pedestrian samples and the non-pedestrian samples, wherein complementary LBP codes in the same defined block are treated as identical LBP codes; (c) expressing the LBP codes in the same defined block as features by a gradient direction histogram (HOG) to obtain feature training samples of the pedestrian samples and the non-pedestrian samples; (d) entering the feature training samples into a SVM to undergo training by Adaboost so as to form a strong classifier; and (e) effectuating pedestrian detection by searching the strong classifiers in thermal images with sliding window technique to detect for presence of pedestrians.

Abstract: An all-weather thermal-image pedestrian detection method includes (a) capturing diurnal thermal images and nocturnal thermal images of a same pedestrian and non-pedestrian object in a same defined block to create a sample database of thermal images, wherein the sample database comprises pedestrian samples and non-pedestrian samples; (b) performing LBP encoding on the pedestrian samples and the non-pedestrian samples, wherein complementary LBP codes in the same defined block are treated as identical LBP codes; (c) expressing the LBP codes in the same defined block as features by a gradient direction histogram (HOG) to obtain feature training samples of the pedestrian samples and the non-pedestrian samples; (d) entering the feature training samples into a SVM to undergo training by Adaboost so as to form a strong classifier; and (e) effectuating pedestrian detection by searching the strong classifiers in thermal images with sliding window technique to detect for presence of pedestrians.

Abstract: A vehicle detection method includes (1) vehicle likelihood region identifying step; (2) vehicle component locating step; and (3) vehicle detecting step. To reduce complexity of calculation and enhance accuracy of detection, the method uses a vehicle likelihood region identifying algorithm to eliminate background regions from a total thermal image and keep vehicle likelihood regions therein for use in further analysis and processing, detects obvious vehicle components, such as vehicle windows and vehicle bottoms, in the thermal image to thereby identify vehicle component likelihood regions, describes a space geometric relationship of vehicle components with a Markov random field model, defines vehicle detection as problems with maximum a posteriori probability, estimates the most likely configuration with an optimization algorithm, so as to effectuate vehicle detection.

Abstract: A vehicle detection method includes (1) vehicle likelihood region identifying step; (2) vehicle component locating step; and (3) vehicle detecting step. To reduce complexity of calculation and enhance accuracy of detection, the method uses a vehicle likelihood region identifying algorithm to eliminate background regions from a total thermal image and keep vehicle likelihood regions therein for use in further analysis and processing, detects obvious vehicle components, such as vehicle windows and vehicle bottoms, in the thermal image to thereby identify vehicle component likelihood regions, describes a space geometric relationship of vehicle components with a Markov random field model, defines vehicle detection as problems with maximum a posteriori probability, estimates the most likely configuration with an optimization algorithm, so as to effectuate vehicle detection.

Abstract: A nocturnal vehicle counting method based on a mixed particle filter is introduced in that, in a nocturnal environment, a rear lamp of a vehicle is the most remarkable feature of the vehicle and forms a high-brightness region of an image of the vehicle. The method involves detecting the high-brightness region of an image of the vehicle to thereby detect the rear lamp of the vehicle. The method further involves operating a particle filter structure which, coupled with the detection of a moving high-brightness region, can detect and track the rear lamp of the vehicle simultaneously, thereby enhancing competitiveness and incurring low costs.

Abstract: A method for controlling reflectivity in imaging system has steps of establishing a model describing the input image is a linear combination of a body reflection and an interface reflection; eliminating the minimum component of RGB for each pixel of the input image, and adding a mean of a sum of the minimum RGB chromaticity of the pixels to the input image to be modified; using a threshold strategy to identify at least one highlight region and at least one non-highlight region in input image; reconstructing color information of the at least one highlight region and obtaining a reconstructed image considering as the body reflection; evaluating the weights of the body reflection and the interface reflection; eliminating the interface reflection term of input image and considering the body reflection term as a reference image; and controlling the LCOS reflector to modify the input image according to the reference image.

Abstract: A method for controlling reflectivity in imaging system has steps of establishing a model describing the input image is a linear combination of a body reflection and an interface reflection; eliminating the minimum component of RGB for each pixel of the input image, and adding a mean of a sum of the minimum RGB chromaticity of the pixels to the input image to be modified; using a threshold strategy to identify at least one highlight region and at least one non-highlight region in input image; reconstructing color information of the at least one highlight region and obtaining a reconstructed image considering as the body reflection; evaluating the weights of the body reflection and the interface reflection; eliminating the interface reflection term of input image and considering the body reflection term as a reference image; and controlling the LCOS reflector to modify the input image according to the reference image.

Abstract: A passenger detector includes an image taker, an image processor and a storage unit. The image taker is used for taking an image of a passenger sitting on a seat. The image processor is connected to the image taker. The image processor is used to learn and identify features of the image and possibilities of states of the passenger and integrate the possibilities to select the most likely state of the passenger. The storage unit is connected to the image processor. The storage unit is used to store image data before and after taking the image.

Abstract: A passenger detector includes an image taker, an image processor and a storage unit. The image taker is used for taking an image of a passenger sitting on a seat. The image processor is connected to the image taker. The image processor is used to learn and identify features of the image and possibilities of states of the passenger and integrate the possibilities to select the most likely state of the passenger. The storage unit is connected to the image processor. The storage unit is used to store image data before and after taking the image.