This project explores object tracking of human and vehicle targets in the infrared using Matlab. Using the OTCBVS Benchmark Dataset Collection, tracking of pedestrians are performed in a variety of infrared videos. Tracking is performed in cases with single pedestrians in the scene and also in cases with multiple pedestrians. Tracking approaches is centered on techniques most suited to the infrared. The project will not focus on identification of targets but instead on maintenance of accurate track following the identification. Performance of the developed tracking techniques are analyzed using the ground truth data from the databases. Metrics, such as those from the IEEE workshop on Performance Evaluation of Tracking and Surveillance is used for the analysis. All coding is done in MATLAB.

Object tracking is a technique used in computer vision and image processing applications. This technique fails to track objects when using only visible sensors in situations with illumination variations, occlusion, camouflage, and adverse climatic circumstances. Thermal sensors are fairly illumination invariant. However, in situations where the temperature is not changing much between the foreground and the background, thermal sensors might not provide the best results, as everything might be single color coded. Visible sensors are known to perform better in these conditions. Thus, the joint use of the visible and thermal sensors would be most beneficial in making a robust object tracking system under such challenging conditions. The proposed method performs object tracking by fusing bimodal information using particle filter with structural similarity index metric (SSIM) as a cue as well as a correlation metric for modality selection. The key idea of this method is to adaptively choose the most discriminating modality with respect to the object being tracked. This is performed by calculation of SSIM between reference and successive frames of both the modalities. The method is evaluated by testing on a variety of extremely challenging video sequences in both imaging modalities and has proven to perform better compared to single modality.

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The Arduino Uno Distance Sensor (Sharp IR Distance Sensor) measures distance by emitting infrared light and detecting the reflection. It provides accurate distance readings in various environments, enabling obstacle avoidance, proximity detection, and object tracking in robotics and automation applications.

A Movement Detection System in Night Vision uses infrared cameras and computer vision to detect motion in dark environments. Techniques like Background Subtraction and Optical Flow identify moving objects by comparing frames and tracking pixel changes, enabling accurate real-time surveillance in low-light conditions.

Deep Learning Models: Advanced neural networks, including (CNNs, FCNNs, RCNN, GAN), to extract features and improve object detection, classification, and tracking in real-time. Sensor Fusion: Combining infrared data with other sensor types (e.g., LiDAR, Radar) for enhanced environmental awareness and decision-making.

Programmed a system of sensors(ultrasonic, infrared and esp32 camera) to detect and track objects in real time and map out a 3d environment with precise depth and distance measurements. Integrated the system with an Arduino microcontroller for data processing and control.

Infrared search and track systems are used because the objects that we want to track are very bright in this spectral range. However, due to the size of target's signal the problem of detection and localization target objects in complex background became a challenging task. This paper proposed the algorithm based on the hypothesis that the data may be made up from multi-subspace. Moreover the overlapping edge information is used for improving quality. So the problem can be considered as optimization problem. By applying the alternating direction method of multipliers (ADMM) I built the solution algorithm.