A method for vehicle detection and tracking from a moving camera, comprising: Acquiring video data including a plurality of frames using said camera; comparing a first frame of the acquired video data against a set of one or more vehicle detectors to form vehicle hypotheses, wherein corner detection is used to generate initial left and right vehicle corner hypotheses, and wherein corner detection comprises applying L-shape filters pruning and verifying the vehicle hypotheses using a set of coarse-to-fine constraints to detect a vehicle according to the steps: pruning said initial corner hypotheses by using a vehicle size constraint; then pruning the remaining corner hypotheses by part detectors, wherein part detectors are trained classifiers that identify if an image region around a corner hypothesis belongs to a vehicle; then grouping pairs of the remaining left and right corner hypotheses satisfying the vehicle size constraint into vehicle hypotheses;and then applying whole-appearance classifiers to determine a detection score on all vehicle hypotheses that passes the previous steps, wherein a vehicle hypothesis is rejected if its detection score is below a pre-defined threshold, otherwise, it is kept as a potential vehicle candidate and passed to a tracking and temporal fusion module; tracking the detected vehicle within one or more subsequent frames of the acquired video data by fusing shape template matching with one or more vehicle detectors. Acquiring video data including a plurality of frames using said camera; comparing a first frame of the acquired video data against a set of one or more vehicle detectors to form vehicle hypotheses, wherein corner detection is used to generate initial left and right vehicle corner hypotheses, and wherein corner detection comprises applying L-shape filters pruning and verifying the vehicle hypotheses using a set of coarse-to-fine constraints to detect a vehicle according to the steps: pruning said initial corner hypotheses by using a vehicle size constraint; then pruning the remaining corner hypotheses by part detectors, wherein part detectors are trained classifiers that identify if an image region around a corner hypothesis belongs to a vehicle; then grouping pairs of the remaining left and right corner hypotheses satisfying the vehicle size constraint into vehicle hypotheses;and then applying whole-appearance classifiers to determine a detection score on all vehicle hypotheses that passes the previous steps, wherein a vehicle hypothesis is rejected if its detection score is below a pre-defined threshold, otherwise, it is kept as a potential vehicle candidate and passed to a tracking and temporal fusion module; pruning said initial corner hypotheses by using a vehicle size constraint; then pruning the remaining corner hypotheses by part detectors, wherein part detectors are trained classifiers that identify if an image region around a corner hypothesis belongs to a vehicle; then grouping pairs of the remaining left and right corner hypotheses satisfying the vehicle size constraint into vehicle hypotheses;and then applying whole-appearance classifiers to determine a detection score on all vehicle hypotheses that passes the previous steps, wherein a vehicle hypothesis is rejected if its detection score is below a pre-defined threshold, otherwise, it is kept as a potential vehicle candidate and passed to a tracking and temporal fusion module; tracking the detected vehicle within one or more subsequent frames of the acquired video data by fusing shape template matching with one or more vehicle detectors.