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This work examines the possibility of exploiting, for the purpose of video segmentation to scenes, semantic information coming from the analysis of the visual modality. This information, in contrast to the low-level visual features typically used in previous approaches, is obtained by application of trained visual concept detectors such as those developed and evaluated as part of the TRECVID High-Level...
Trained detectors are the most popular algorithms for the detection of vehicles or pedestrians in video sequences. To speed up the processing time the trained stages build a cascade of classifiers. Thereby the classifiers become more powerful from stage to stage. The most popular classifier for real-time applications is Adaboost applied to rectangular Haar-like features. The processing time of these...
Text detection for video sequences has played an important role in real world applications. In the paper, new text detectors based on text intrinsic structures were proposed. Temporal information was employed to remove false positive features. And density-based method was introduced as post-processing step to filter out noises. Experimental results show that proposed approach could obtain challenging...
We have proposed a complete system for text detection and localization in gray scale scene images. A boosting framework integrating feature and weak classifier selection based on computational complexity is proposed to construct efficient text detectors. The proposed scheme uses a small set of heterogeneous features which are spatially combined to build a large set of features. A neural network based...
Video analytics have recently emerged as a promising technique of retail fraud detection for loss prevention. Efficient video analytic algorithms are highly desired for a practical fraud detection system. In this paper, we present a real-time algorithm for recognizing a cashier's actions at the point of sale (POS), which can be further used to analyze cashier behaviors for identifying fraudulent incidents...
We investigate the problem of automatically labelling faces of characters in TV or movie material with their names, using only weak supervision from automatically-aligned subtitle and script text. Our previous work (Everingham et al. [8]) demonstrated promising results on the task, but the coverage of the method (proportion of video labelled) and generalization was limited by a restriction to frontal...
A detection-based paradigm decomposes a complex system into small pieces, solves each subproblem one by one, and combines the collected evidence to obtain a final solution. In this study of video story segmentation, a set of key events are first detected from heterogeneous multimedia signal sources, including a large scale concept ontology for images, text generated from automatic speech recognition...
This paper proposes a robust video fingerprinting method based on 2-dimensional oriented principal component analysis (2D-OPCA) of affine covariant regions. The goal of video fingerprinting is to identify a video clip using perceptual features called fingerprints. In the proposed method, to achieve the robustness against geometric transformations, fingerprints are extracted from local regions co-...
In this paper we describe collaborative and integrative work in the K-Space Network of Excellence. A goal of the work presented consists of combining results of the analysis of soccer videos with the semantic analysis of textual complementary sources, in order to support the semantic annotation and indexing of soccer videos. We present briefly a former approach to text-based semantic annotation and...
In this demonstration we showcase an interactive analysis tool for researchers working on concept-based video retrieval. By visualizing intermediate concept detection analysis stages, the tool aids in understanding the success and failure of video concept detection methods. We demonstrate the tool on the domain of pop concert video.
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