Shot boundary detection is a fundamental step of video indexing. One crucial issue of this step is the discrimination of abrupt shot change from flashlight, because flashlight often induces a false shot boundary. Support vector machine (SVM) is a supervised learning technique for data classification. In this paper, we propose a SVM-based technique to detect flashlights in video. Our approach to flashlight detection is based on the facts that the duration of flashlight is short and the video contents before and after a flashlight should be similar. Therefore, we design a sliding window in temporal domain to monitor the instantaneous video variation and extract color and edge features to compare the visual contents between two video segments. Then, a SVM is employed to classify the luminance variation into flashlight or shot cut. Experimental results indicate that the proposed approach is effective and outperforms some existing techniques.

In this paper, we propose an integrated approach for human detection in surveillance video. In our approach, the moving object is extracted by background subtraction; and the background model is updated by the first-order recurrence filter. Then, two complementary features are extracted for moving object classification. They are contour-based description: Fourier descriptor and region-based description: histogram of oriented gradient. As the binary classifier (support vector machine) is able to provide the posterior probability, we effectively integrate two types of features to achieve better performance. Experimental results show that the proposed approach is effective and outperforms some existing technique.

In this paper, we propose a novel method for automatically locating the skyline that represents the shape of mountains. The appearances of mountain and sky are variable because of the weather, season or region. In order to extract the skyline of mountains under complicated and variable circumstances, support vector machine (SVM) is applied for the prediction of a part of the skyline between sky region and mountain region by using the color, statistics features and location information of edge. Then, the linking of incomplete fragments of skyline is formulated as a shortest path problem and solved by dynamic programming strategy. Our experimental results demonstrate that the proposed method is accurate and robust.

The usefulness of an image database depends on whether the image of interest can be easily located. Feature extraction is a crucial step of image retrieval. The well-known SIFT descriptor is a keypoint-based image feature. It can be used to robustly find the same objects in different images, i.e. achieve the object recognition task. However, it is not effective on the image retrieval task, i.e. finding images with similar content. To improve the SIFT algorithm, we propose a robust image retrieval algorithm based on the integration of keypoints and edges information. Our approach is robust to translation, rotation and partial occlusion of the object. Experimental results indicate that the proposed algorithm is effective and outperforms the SIFT algorithm.

As violence in movies has harmful influence on children, in this paper, we propose a vision-based algorithm to detect violent scenes in movies. Under our definition of violence, the task of violent scene detection is decomposed into action scene detection and bloody frame detection. While previous approaches addressed only the shot level of video structure, our approach works on a more semantic-complete scene structure of video. The input video (digital movie) is first segmented into several scenes. Based on the filmmaking characteristics of action scene, some features of the scene are extracted to be fed into the support vector machine for classification. Finally, the face, blood and motion information are integrated to determine whether the action scene has violent content. Experimental results show that the proposed approach works reasonably well in detecting most of the violent scenes. Compared with related work, our approach is computationally simple yet effective.

This paper presents a novel framework for object completion in a video. To complete an occluded object, our method first samples a 3-D volume of the video into directional spatio-temporal slices, and performs patch-based image inpainting to complete the partially damaged object trajectories in the 2-D slices. The completed slices are then combined to obtain a sequence of virtual contours of the damaged object. Next, a posture sequence retrieval technique is applied to the virtual contours to retrieve the most similar sequence of object postures in the available non-occluded postures. Key-posture selection and indexing are used to reduce the complexity of posture sequence retrieval. We also propose a synthetic posture generation scheme that enriches the collection of postures so as to reduce the effect of insufficient postures. Our experiment results demonstrate that the proposed method can maintain the spatial consistency and temporal motion continuity of an object simultaneously.

Video inpainting is an important video enhancement technique used to facilitate the repair or editing of digital videos. It has been employed worldwide to transform cultural artifacts such as vintage videos/films into digital formats. However, the quality of such videos is usually very poor and often contain unstable luminance and damaged content. In this paper, we propose a video inpainting algorithm for repairing damaged content in digitized vintage films, focusing on maintaining good spatiotemporal continuity. The proposed algorithm utilizes two key techniques. Motion completion recovers missing motion information in damaged areas to maintain good temporal continuity. Frame completion repairs damaged frames to produce a visually pleasing video with good spatial continuity and stabilized luminance. We demonstrate the efficacy of the algorithm on different types of video clips.

In this paper, we propose a new object detection method that does not need a learning mechanism. Given a hand-drawn model as a query, we can detect and locate objects that are similar to the query model in cluttered images. To ensure the invariance with respect to rotation, scaling, and translation (RST), high curvature points (HCPs) on edges are detected first. Each pair of HCPs is then used to determine a circular region and all edge pixels covered by the circular region are transformed into a polar histogram. Finally, we use these local descriptors to detect and locate similar objects within any images. The experiment results show that the proposed method outperforms the existing state-of-the-art work.

This paper presents a novel framework for object-based video inpainting. To complete an occluded object, our method first samples a 3-D volume of the video into directional spatio-temporal slices, and then performs patch-based image inpainting to repair the partially damaged object trajectories in the 2-D slices. The completed slices are subsequently combined to obtain a sequence of virtual contours of the damaged object. The virtual contours and a posture sequence retrieval technique are then used to retrieve the most similar sequence of object postures in the available non-occluded postures. Key-posture selection and indexing are performed to reduce the complexity of posture sequence retrieval. We also propose a synthetic posture generation scheme that enriches the collection of key-postures so as to reduce the effect of insufficient key-postures. Our experimental results demonstrate that the proposed method can maintain the spatial consistency and temporal motion continuity of an object simultaneously.

This paper presents a novel people counting system for an environment in which a stationary camera can count the number of people watching a TV-wall advertisement or an electronic billboard without counting the repetitions in video streams in real time. The people actually watching an advertisement are identified via frontal face detection techniques. To count the number of people precisely, a complementary set of features is extracted from the torso of a human subject, as that part of the body contains relatively richer information than the face. In addition, for conducting robust people recognition, an online classifier trained by Fisher's Linear Discriminant (FLD) strategy is developed. Our experiment results demonstrate the efficacy of the proposed system for the people counting task.

In this paper, we propose a vision-based people counting system. The symmetry property of human torso is utilized for the detection of people. Using the symmetry property as feature, one can solve the incomplete extraction of human silhouette problem that is commonly encountered in the process of background subtraction. In addition, since the perspective projection effect may influence the orientation of principal axis of a torso, we also propose a solution to address this issue. The experiment results show that the proposed system can perform accurate people counting.

In this paper, we propose the use of motion vectors embedded in MPEG bitstreams to generate so-called ldquomotion flowsrdquo, which are applied to perform video retrieval. By using the motion vectors directly, we do not need to consider the shape of a moving object and its corresponding trajectory. Instead, we simply ldquolinkrdquo the local motion vectors across consecutive video frames to form motion flows, which are then recorded and stored in a video database. In the video retrieval phase, we propose a new matching strategy to execute the video retrieval task. Motions that do not belong to the mainstream motion flows are filtered out by our proposed algorithm. The retrieval process can be triggered by query-by-sketch or query-by-example. The experiment results show that our method is indeed superb in the video retrieval process.

In recent years, real-time direct detection of events by surveillance systems has attracted a great deal of attention. In this paper, we propose a new video-based surveillance system that can perform real-time event detection. In the background modeling phase, we adopt a mixture of Gaussian approach to determine the background. Meanwhile, we use color blob-based tracking to track foreground objects. Due to the self-occlusion problem, the tracking module is designed as a multi-blob tracking process to obtain similar multiple trajectories. We devise an algorithm to merge these trajectories into a representative one. After applying the Douglas-Peucker algorithm to approximate a trajectory, we can compare two arbitrary trajectories. The above mechanism enables us to conduct real-time event detection if a number of wanted trajectories are pre-stored in a video surveillance system.

Gradual shot change detection is one of the most important research issues in the field of video indexing/retrieval. Among the numerous types of gradual transitions, the dissolve-type gradual transition is considered the most common one, but it is also the most difficult one to detect. In most of the existing dissolve detection algorithms, the false/miss detection problem caused by motion is very serious. In this paper, we present a novel dissolve-type transition detection algorithm that can correctly distinguish dissolves from disturbance caused by motion. We carefully model a dissolve based on its nature and then use the model to filter out possible confusion caused by the effect of motion. Experimental results show that the proposed algorithm is indeed powerful.

Gradual shot change detection is one of the most important research issues in the field of video indexing/retrieval. Among the numerous types of gradual transitions, dissolve is considered the most common one, but also the most difficult to be detected one. It's well known that an efficient dissolve detection algorithm which can be executed on a real video is still deficient. In this paper, we present a novel dissolve detection algorithm that can efficiently detect dissolves with different durations. In addition, global motions caused by camera movement and local motions caused by object movement can be discriminated from a real dissolve by our algorithm. The experimental results show that the new method is indeed powerful.