An adaptive threshold shot detection algorithm based on improved block color features

Shot boundary detection has attracted much more research interesting in recent years, but many of the algorithms proposed in the literatures are not robust to flash lamp and motion of camera and object. This paper present a novel shot boundary detection algorithm based on graph theory. It can detect cut and gradual shot and distinguish them, and it is not intensive to all of those interference factors. At first the feature of color is extracted, the dissimilarity of video frames is defined. Then the video frames are divided into several different groups through performing graph-theoretical algorithm. According to cut and gradual changes have the different characters on the two successive frames belong to different groups, it detect cut and gradual shot. The efficiency of the proposed algorithm is extensively tested on movie, sports and other videos. The experiments results indicate the method had a high accurate rate in both cut shot detection and gradual shot detection.

Shot boundary detection is essentially to detect the position of frames where the shot changes. It has been actively studied in video analysis and management for convenience, which becomes a key technique with the rapid proliferation of rich and diverse videos. With respect to the complex characteristics of different shots in varying length and content variation property, in this paper we present a two stage method for shot boundary detection (TSSBD) which distinguishes abrupt shot by fusing color histogram and deep features, and locate gradual shot changes with C3D-based deep analysis. Abrupt shot changes are detected firstly as it occurs between two frames, which divides the complete video into segments containing gradual transitions; Over these video segments, gradual shot change detection is implemented using 3D-convolutional neural network, which classifies clips into specific gradual shot change types; Finally, an effective merging strategy is proposed to locate positions of gradual shot transitions. The experimental analysis illustrates that the proposed progressive method is capable of detecting both abrupt shot transitions and gradual shot transitions accurately.

Recent advances in computing, communication, and sensor technology are pushing the development of many new applications. This trend is especially evident in pervasive computing. The content-based video analysis is one example of this innovation. Shot boundary detection servers as an important step to structure the contents of videos, many research efforts have been devoted to this field. A novel approach is proposed to detect cuts and gradual shots. An effective feature selection mechanism is introduced using shot boundary coefficient (SBC) combined with HSV divisional histogram. SBC is derived from a model called average sliding-window difference. It is a novel way to enlarge frame difference. SBC can remove the factors affected by camera/object motion, illumination and other artifacts on the curve of frame-wise histogram difference. Each area of the frame has different weight according to its respective importance. In order to improve the speed, at the beginning of the program, we adopt n rank Newton interpolation formula to deflate the frame size. We use Self-Organizing Map (SOM) Network to detect the abrupt shot. It can avoid the threshold completely. Gauss Model is selected to detect gradual shots. Based on the set of abrupt shots, we qualify the candidate set of the gradual shot furthermore. The experimental results have shown that a good compromise between speed and accuracy is achieved. It also has good noise tolerance characteristics.

Shot change detection is a main technique for automatic temporal segmentation of video, which requires adaptive adjustment of thresholds and real-time operation in actual applications. To date, few researches have been conducted on real-time shot change detection to be applied to hardware terminals with low performance such as PMPs (portable media player) and cellular phones. In this paper, we propose an adaptive real-time shot change detection algorithm and implement it on an actual PMP. The proposed algorithm consists of the following elements: sub-sampling, weighting variance, and adaptive thresholds. Our experiments obtained the detection rate of about 94.4% in overall accuracy, demonstrating a higher detection rate and a computational amount 1/256 less than the conventional algorithms which have optimal fixed thresholds adjusted by humans for each video. We verified the real-time operation of shot change detection by implementing our algorithm on the PMP of Homecast, a PMP company. The proposed algorithm will be helpful in searching video data on portable media players such as PMPs and cellular phones.

Shot change detection is the main technique for automatic temporal segmentation of video. Until now, there were little research reports about real-time shot change detection for applying to hardware terminals with low performance such as PMPs (Portable Multimedia Player) and cellular phones. In this paper, we propose an adaptive real-time shot change detection algorithm using sub-sampling, weighting variance, and an adaptive threshold. We use weighting variance of frame difference for dividing feature extraction at a changed frame. We set an adaptive threshold using the average of variance from the next one of a last shot change frame to the previous one of a current frame. In the experiments, we obtained the detection rate of about 94.4% in overall accuracy. We verified real-time operation of shot change detection by implementing our algorithm on the TVUS HM-900 PMP from the company of Homecast. Therefore, our proposing algorithm will be helpful in searching video data on portable media player such as PMPs and cellular phones.

As the fast development of network and multimedia technology, there are mass of videos on the Internet. The problem of how to browse the large volume of video data is urgent to be solved. Key frame extraction is a very important technology in online video retrieval and video abstraction. Efficient key frame extraction can boost the development of video browsing technology. In this paper, we first review some commonly-used key frame extraction technology, and then propose a improved key frame extraction algorithm, which measures the similarity of two adjacent frames' contents in terms of the information of frame difference and extracts key frames after optimizing the frame difference. The experiment results show that the proposed algorithm can extract key information from online video efficiently, and has a good description in the aspect of motion of the camera lens.

In this paper, we propose a deinterlacing method with motion adaptive vertical temporal filtering, which utilizes the correlations between adjacent frames. In this model, we first interpolate the missing lines of the current frame and adjacent frames by using an intrafield deinterlacing method. Then we compute the pixel differences between the current frame and the adjacent frames. Since the differences between the adjacent frames would show similar patterns, we can use these patterns to improve deinterlacing performance. In other words, instead of performing deinterlacing in the frame domain, we perform the operation in the frame difference domain. Since the proposed method produces good performance in stationary regions, we selectively apply the vertical temporal filter. Then we apply the proposed method iteratively in order to enhance video quality. The proposed method shows low complexity and still produces superior performance. Experimental results show that the proposed method provides noticeable improvements over existing methods in terms of both subjective and objective evaluations.

This paper presents a method for localizing noise-corrupted areas in quality degraded video frames, and for reducing the additive noise by utilizing the temporal redundancy in the video sequence. In the proposed algorithm, the local variance of each pixel is computed to obtain the spatial distribution of noise. After adaptive thresholding, region clustering, and merging, the corrupted areas of highest energy are detected. Due to the high temporal redundancy in the video sequence, the corrupted information can be compensated by overlapping the corrupted regions with the appropriate regions from adjacent video frames. The corresponding pixel locations in the adjacent frames are computed by using image registration and warping techniques. New pixel values are calculated based upon multi-frame stacking. Pixel values in the adjacent frames are weighted according to registration errors, whereas the values in the noisy frame are evaluated according to local variance. Knowing the location of the local noise enables the denoising process to be much more specific and accurate. Moreover, since only a portion of the frame is processed, as compared to standard denoising methods that operate on the entire frame, the details and features in other areas of the frame are preserved. The proposed scheme is applied to UAV video sequences, where the outstanding noise localization and reduction properties are demonstrated.

Video segmentation is fundamental to a number of applications related to video retrieval and analysis. Shot change detection is the initial step of video segmentation and indexing. There are two basic types of shot changes. One is the abrupt change or cut, and the other is the gradual shot transition. The smooth variations of the video feature values in a gradual transition produced by the editing effects are often confused with those caused by camera or object motions. To overcome this difficulty, it is reasonable to estimate the motions and suppress the disturbance caused by them. In this thesis, we explore the possibility to exploit motion and illumination estimation in a video sequence to detect both abrupt and gradual shot changes. A generalized optical flow constraint that includes an illumination parameter to model local illumination changes is employed in the motion and illumination estimation. An iterative process is used to refine the generalized optical flow constraints step by step. A robust measure that is the likelihood ratio of the corresponding motion-compensated blocks in the consecutive frames is used for detecting abrupt changes. For the detection of gradual shot transitions, we compute the average monotony of intensity variations on the stable pixels in the images in a twin-comparison framework. We test the proposed algorithm on a number of video sequences in TREC 2001 and compare the detection results with the best results reported in the TREC 2001 benchmark. The comparisons indicate that the proposed shot change detection algorithm is competitive against the best existing algorithms.

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Smartphone cyber-attacks are one of the major security threats of the present-day mobile computing era. Such attacks are used on the weaknesses that arise upon initial device configuration and they tend to extract sensitive data of the users, the system integrity and the functionality of a given device. This paper presents the post-installation attack vectors and detection together with a new Dynamic Security Assessment Framework (DSAF) that can be used to detect and mitigate the attacks in real time. The proposed method integrated behavior analysis, machine learning, and anomaly detection methods to detect post-installed suspicious activity. The proposed method uses two prominent algorithms, i.e., the Adaptive Threat Detection Algorithm (ATDA) and the Risk-Based Response Algorithm (RBRA). We have carried out an experimental study that shows that our strategy reaches a 94.7 percent accuracy in detection with a false positive of 2.3 percent that is much higher than what the current security solutions are capable of. The effectiveness of the framework is proved through thorough testing on 1,500 smartphones of various platforms whereby a 78 percent post-installation invasion is curbed using the framework as compared to conventional security systems.

Track detection systems based on ultrasonic guided waves (UGWs) for continuous welded rails have achieved a rapid development in recent years. Though these systems can work properly in normal track conditions, UGW signals cannot penetrate the gap caused by a track breakage. To overcome this issue, a novel track circuit system based on a previous UGW system has been proposed in this paper. The 63-bit Kasami codes have been applied to the transmission scheme. The auto- and cross-correlation properties from Kasami codes have allowed the transmission detection at the receiver end, even under adverse conditions, such as noise or high attenuation. Furthermore, in previous track circuits, the correct detection and identification of the corresponding transmissions at the receivers had become a key aspect for the suitable and reliable operation of such type of systems. In this way, in addition to describing a novel track circuit, this paper focuses on the proposal of a novel adaptive peak detection algorithm to discriminate the main lobes in the auto-correlation functions at the receiver, thus making possible to correctly determine the track status in future works. This algorithm is mainly based on the discrete wavelet transform, on a moving average filter, as well as on the Hilbert Transform. Through the usage of a simulation model for the proposed track circuit as well as some outdoor experimental tests, the accuracy and the stability of the adaptive peak detection algorithm for the novel track circuit have been verified.

In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A color deviation histogram (CDH) is also proposed to eliminate confusing backgrounds and refine optimal thresholds. The selection process of optimal thresholds is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.

In this paper, we propose adaptive texts detection and localization algorithms based on an edge extraction approach. The main techniques include edge filtering, adaptive threshold, the morphological operator and pixel correlation. With block-based processing, the rough texts can be detected by edge strength. The morphological operation is employed to refine the text region and to remove discrete non-text marks as well. The final text region can be accurately localized with a rectangle by horizontal differential approach. Simulations show that the proposed method can attain higher accuracy and lower false detection compared with the recent top paper.

With the development of multimedia technology and the appearance of information highway, the storage and transmission of digital video have become a less difficult issue. Various sport game brings the explosive expanding of sport video, therefore, efficient analysis of sport video was necessary. An algorithm of shot segmentation was proposed. Firstly, the data in each frame was quantized in HSV model; then based on histogram of each frame, the difference of adjacent frames, the difference of cumulate frames and the percentage of the frame difference in a defined window were computed; furthermore, the adaptive average in a window, the adaptive thresholds and sliding windows were obtained. Experiment results show that the new approach is superior over double threshold comparison method and singular value decomposition algorithm.