This paper proposes a method for detecting spatio-temporal interest points, which extend the concept of spatial interest points to the spatio-temporal domain. The method is based on the Harris and Förstner interest point operators and detects local structures in space-time where image values have significant variations in both space and time. The detected events are then characterized by their spatio-temporal extents and scale-invariant descriptors. These descriptors are used to classify events and construct video representations in terms of labeled space-time points. The method is demonstrated for human motion analysis, where it allows for the detection of walking people in scenes with occlusions and dynamic backgrounds. The paper describes the detection of interest points in the spatio-temporal domain by extending the Harris corner function to include temporal variations. The method involves computing the spatio-temporal second-moment matrix and using the determinant and trace of this matrix to detect interest points. The method is tested on synthetic sequences and is shown to effectively detect events such as moving corners, collisions, and merges. The method also includes a scale selection process to estimate the spatio-temporal extent of events. The paper further discusses the classification of events using k-means clustering and spatio-temporal descriptors. The method is applied to video interpretation, where it is used to detect walking people and estimate their poses in outdoor scenes. The method is shown to be effective in complex scenes with dynamic backgrounds and occlusions. The results demonstrate that the method is robust to variations in size and can accurately detect and classify events in video data. The paper concludes that the method provides a stable and efficient way to represent and interpret video data by detecting spatio-temporal events.This paper proposes a method for detecting spatio-temporal interest points, which extend the concept of spatial interest points to the spatio-temporal domain. The method is based on the Harris and Förstner interest point operators and detects local structures in space-time where image values have significant variations in both space and time. The detected events are then characterized by their spatio-temporal extents and scale-invariant descriptors. These descriptors are used to classify events and construct video representations in terms of labeled space-time points. The method is demonstrated for human motion analysis, where it allows for the detection of walking people in scenes with occlusions and dynamic backgrounds. The paper describes the detection of interest points in the spatio-temporal domain by extending the Harris corner function to include temporal variations. The method involves computing the spatio-temporal second-moment matrix and using the determinant and trace of this matrix to detect interest points. The method is tested on synthetic sequences and is shown to effectively detect events such as moving corners, collisions, and merges. The method also includes a scale selection process to estimate the spatio-temporal extent of events. The paper further discusses the classification of events using k-means clustering and spatio-temporal descriptors. The method is applied to video interpretation, where it is used to detect walking people and estimate their poses in outdoor scenes. The method is shown to be effective in complex scenes with dynamic backgrounds and occlusions. The results demonstrate that the method is robust to variations in size and can accurately detect and classify events in video data. The paper concludes that the method provides a stable and efficient way to represent and interpret video data by detecting spatio-temporal events.