Dark infrared night vision imaging proposed work for pedestrian detection and tracking

This framework presents three efficient proposed algorithms for pedestrian detection and tracking in Dark Infrared Night Vision (DIRNV) images. The first approach is relied on Gradient Estimation (GE) after mixing structure Equalization Exponential Contrast Limited Adaptive Histogram Equalization (ECLAHE) with Gamma Correction, and finally Cumulative Histogram (GECUGC) for discrimination. The GECUGC relies on enhancement using mixing ECLAHE Using Gamma Correction (ECUG) in addition to pre-processing followed by the GE using Laplacian Filter (LAF), and finally Cumulative Histograms (CH) for the detection or classification task. The second approach is based GE after a hybrid structure Histogram Equalization (HE) with Nonlinear Technique and finally CH (GHNTC) for discrimination. The GHNTC depends on enhancement by merging HE with Nonlinear Technique (NT) (HENT) followed by the GE using LAF and finally CH for pedestrian detection and tracking using DIRNV imaging. After the CH estimation, the difference between cumulative histograms with and without objects is estimated and used for pedestrian detection and tracking using DIRNV imaging. The third algorithm is based scale space analysis with the number of the Speeded Up Robust Features (SURF) points as the key parameters for classification. This technique is presented to detect the features of DIRNV pedestrian images and tracking. The performance metrics are the difference area between the cumulative histograms of DIRNV images with and without pedestrian, computation time, points of features and speed up factor. Simulation results prove that the success of three suggested techniques in pedestrian detection and tracking using DIRNV imaging. By comparing the three presented algorithms, it is clear that the second suggested technique gives superior for pedestrian detection and tracking from point view difference area between the cumulative histograms.On the other hand the first suggested technique is the best algorithms for pedestrian detection and tracking from point view the computation time. The obtained results clear that the third approach has sucesseded in gait pedestrian detection and tracking using DIRNV imaging.