Language based object detection with human body using CLIPseg : Integrating CLIP and Optical Flow for Unsupervised Object Tracking and Keypoint Detection

Human pose estimation and keypoint detection are essential tasks in computer vision, with widespread applications in areas such as action recognition, human-computer interaction, sports analytics, and medical imaging. Recent advances in deep learning and optical flow models have revolutionized these fields, enhancing the precision and scalability of tracking human motion. In areas that're not human related or in the field of object tracking in general Challenges arise in settings calling for adaptable frameworks. The main focus of this research is, on understanding how to accomplish recognition. Monitoring of moving points, on various objects without the need, for manual intervention. We demonstrate how combining CLIP and CLIPSeg models, with optical flow enhances annotations. With methods, like Co tracker and Dense Optical Flow, at our disposal we can carry out tasks without supervision. keypoint identification and continuous tracking, across frames, in a video sequence are highlighted in our findings showcasing. This method effectively removes the necessity, for annotations while still upholding standards. Significant progress has been made in identifying and monitoring points accurately marking an advancement, over previous methods. current approaches that heavily depend on existing labeled data. This represents a significant improvement over existing methods that heavily rely on pre-labeled data. The integration of optical flow with CLIP-based models allows for real-time tracking and motion prediction in complex, dynamic scenes, extending the applicability of keypoint detection beyond human pose estimation to arbitrary objects. This approach has broad implications for real-world applications such as video surveillance, autonomous navigation, and sports performance analysis, where unsupervised tracking can provide greater adaptability and efficiency. By eliminating manual intervention, our method sets a new standard for scalability and accuracy in dynamic tracking tasks.