No abstract

Target tracking is an important field that attracted a lot of interest in computer vision. This paper describes an end-to-end technique for extracting moving targets from a real-time video stream and real sequence of live camera when the appearance of the object changes. Targets are classified into predefined categories according to image based properties, and then robustly tracking them. However, since the scale of the targets often varied irregularly, systems with fixed-size tracking window usually could not accommodate to these scenarios. In present paper, a modified approach of tracking algorithm with Self-Updating Tracking was introduced. The proposed algorithm divided into two stages, the first stage depending on detecting the sharp corner of moving target based on Lucas-kanade techniques. The second stage detecting the color of target being detected in the previous stage and able to track the detected color belonged to moving target with adaptive window size. Experimental results demonstrated that the improved algorithms could select the proper size of tracking window not only when the object scale increases but the scale decreases as well with minor extra computational overhead.

This paper introduces a proposed modified approach of an end-to-end technique for moving targets tracking. The tracking technique is processed on a real-time video stream. The proposed approach is a prolongation of the Continuously Adaptive Mean Shift (CAMShift) algorithm applications. Sever variations in target shape, size and luminosity can be dealt better using this algorithm. Edge detection technique is used to deal with the change in target shape and size. An estimator is used to deal with luminosity changes. A proper size of tracking window is built with minor extra computational overhead. Experimental results show the effectiveness of the proposed algorithm.

This paper describes and evaluates a number of techniques for reducing different types of noises which associated with the thermal images. These techniques are based on optical image filtering in both spatial domain and frequency domain. Filtering in both spatial domain and frequency domain are applied on different thermal images associated with three standard noises models encountered in most images as additive, multiplicative, and impulse noises with different variance. Also, Non-uniformity correction techniques are applied on several thermal images associated with Fixed Pattern Noise (FPN). The algorithms have been tested by using several real image data from existing infrared imaging systems with good results. Measuring criteria for performance evaluation of thermal images enhancement techniques as Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE) are used to ensure the vision observation of user to select the most suitable technique with highly performance evaluation.

In this paper, we present a new approach for tracking a moving target in forwardlooking infrared (FLIR) imagery taken from an anti-tank moving platform. First, the target image is captured in a frame with resolution 128×128 pixels by using IR camera, and then the image is enhanced by using a non uniformity correction algorithm to reduce the main dominant noises associated with infrared images. Second, the existing moving target is detected with time ( ) within the image by using 2-D Gabor filter kernels. Next, Gabor filter with 4 orientations applying on the window created with resolution 32×32 pixels around the predicted target center using Kalman filter estimator. Subtraction process is performed between the predicted center using Kalman filter estimator and the center of the detected target using Gabor filter, which is used to predict the next center by using Kalman filter, and to guide the detection of the target location in the tracking window of the next frame respectively. The proposed technique fasts and reduces the detection time into 1/16 of with high performance of tracking. The experiments performed on several real image data of an existing infrared imaging system. Results show the robustness of the proposed method, which combines high speed of detection and good performance of tracking even with strong ego-motion.