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# On Road Vehicle Breakdown Assistance Finder Project

## Appearance Based Methods in Vehicle detection

Motion Based Models: Motion based methods extract moving vehicles based on motion from background. Motion based methods includes Temporal frame differencing and Back ground subtraction.A. Frame differencing:Frame differencing is least complex and quickest method. Pixel wise difference is figured between two back to back frames. Moving foreground regions are determined using a threshold value . The detection can be improved using three consecutive frames. Dual inter frame subtraction followed bitwise AND is performed to extract the moving object . B. Background subtraction: Foreground objects are separated by computing pixel wise distinction between the present image and the static background image . The information about the background is accumulated to produce the background model. Background can be parametric or non-parametric. •Parametric Models: Parametric model uses a uni– model probability on each pixel and update the distribution parameters. Frame averaging: Frame averaging is a conventional averaging technique where a set of frames are averaged. The resulting background model will be subtracted from consecutive frames this technique has high computational efficiency but has tail effects . The accuracy depends on N which comes at the cost of memory requirements. Single Gaussian: Background is modeled recursively using single Gaussian. This improves robustness and reduces the memory requirements. The background is computed recursively in terms of cumulative running average and standard deviation . Each pixel is distinguished into background orforeground depending on the pixel position in Gaussian distribution. This model reduces the cost but tail effect still persists. Median Filter: The background is evaluated by locating the median value for each pixel from a set of frames . This technique is adopted when background pixels will not vary rapidly with time. A recursive approximation approach estimate the median using recursive filter which increases or decreases by one whenever input pixel has a value greater or less than estimate and is not changed if equal. Gaussian Mixture Model: Gaussian mixture models each pixel as a blend of two or more Gaussian temporally updated . The stability is evaluated to estimate the distribution as table background or short- term foreground process. •Non parametric Models: Non parametric background models use pixel history to construct a probabilistic model of the observation using recent samples of pixel values and do not consider pixel value as particular distribution . Kernel Density Estimation (KDE) and codebook are non-parametric models. Kernel Density Estimation (KDE): the nonparametric KDE characterize a multimodal probability density function . The probability of each background pixel is estimated from recent samples using Parzen window. Codebook Model: Parameters represented by probabilistic function is replaced by a set of dynamically handled code word to model background . Quantization or clustering is applied after this. Each codebook contains a number of code words. The new pixel is classified as background if the value of the pixel belongs to the code word range else classified asforeground