Object tracking plays a vital role in many computer vision systems applications, such as video surveillance, robotics, 3-D image reconstruction, medical imaging, human computer interface, etc. In many proposed approaches, feature-based object tracking is widely used due to its accuracy. Feature extraction and feature correspondence are two main components of feature-based object tracking. In our proposed method, we have used gray level co-occurrence matrix (GLCM) features for object tracking in thermal imagery. As spatial resolution of the thermal sensor is fairly coarse, it also implies that temperature scales are close but may not exactly be the same, which indicates the presence of mutually related pixels or group of pixels. The GLCM texture analysis is based on assumption that the texture information of an image is an average spatial relationship between the gray tones in the image. Thus, this similarity in spatial resolution properties makes GLCM features suitable for object tracking in thermal infrared imagery. Initially, the target blobs to be tracked are provided by object detection stage. Then, for a given target blob in a frame, we first calculate GLCM feature points and then find corresponding features in the next successive frame. The sum of squared differences between two feature point sets is calculated to find feature correspondence between two frames for object tracking. Simultaneously, the codebook of the center of the blobs for prediction of the target candidate region in the next frame is maintained in order to have robust object tracking under occlusions. GLCM-based object tracking in thermal imagery outperforms the color or LBP-based mean-shift approach. This algorithm is also able to track objects in split and merge condition. The accuracy of this algorithm also depends on the object detection stage, such as Kalman tracking.
Moving object detection is one of the most promising research areas, which is required in different applications, such as video monitoring and surveillance systems, human activity recognition systems, vehicle counting, and anomaly detection. Various methods for object detection using single sensor and a few using multimodal techniques have been reported in the literature. However, such systems fail to handle adverse or challenging atmospheric conditions such as illumination variations, scale and appearance change of objects or targets, occlusions, and camouflaged conditions. We have presented an approach for the detection of moving objects using structural similarity metric (SSIM) and Gaussian mixture model (GMM). SSIM is used to compute similarity between reference mean background frame and foreground frame of visible spectrum (VIS) and thermal infrared (IR) independently. The computation of similarity measure is performed in an image spatial domain. The threshold results of SSIM are fused together using different pixel-level fusion methods such logical “OR,” discrete wavelet transform, and principal components analysis. Temporal analysis is performed to eliminate noise and false positives (unwanted background regions) using GMM on fused results. We have compared the results with recent methods for different complex scenarios and found out that approximately F-measure increases up to 80%. Hence, the proposed method proves to be a robust moving object detection technique in multimodality domain.