Jinqiao Wang

Real-Time Probabilistic Covariance Tracking With Efficient Model Update

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

Real-time visual tracking via Incremental Covariance Tensor Learning

Visual tracking is a challenging problem, as an object may change its appearance due to pose variations, illumination changes, and occlusions. Many algorithms have been proposed to update the target model using the large volume of available information during tracking, but at the cost of high computational complexity. To address this problem, we present a tracking approach that incrementally learns a low-dimensional covariance tensor representation, efficiently adapting online to appearance changes for each mode of the target with only ̃(1) computational complexity. Moreover, a weighting scheme is adopted to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Tracking is then led by the Bayesian inference framework in which a particle filter is used to propagate sample distributions over time. With the help of integral images, our tracker achieves real-time performance. Extensive experiments demonstrate the effectiveness of the proposed tracking algorithm for the targets undergoing appearance variations.

Semantic Event Extraction from Basketball Games using Multi-Modal Analysis

In this paper, we present a novel multi-modal framework for semantic event extraction from basketball games based on Webcasting text and broadcast video. We propose novel approaches to text analysis for event detection and semantics extraction, video analysis for event structure modeling and event moment detection, and text/video alignment for event boundary detection in the video. Compared with existing approaches to event detection in sports video which rely heavily on low-level features directly extracted from video itself, our approach aims to bridge the semantic gap between low-level features and high-level events and facilitates personalization of the sports video. Promising results are reported on real-world video clips by using text analysis, video analysis and text/video alignment.

A Robust Method for TV Logo Tracking in Video Streams

Most broadcast stations rely on TV logos to claim video content ownership or visually distinguish the broadcast from the interrupting commercial block. Detecting and tracking a TV logo is of interest to TV commercial skipping applications and logo-based broadcasting surveillance (abnormal signal is accompanied by logo absence). Pixel-wise difference computing within predetermined logo regions cannot address semi-transparent TV logos well for the blending effects of a logo itself and inconstant background images. Edge-based template matching is weak for semi-transparent ones when incomplete edges appear. In this paper we present a more robust approach to detect and track TV logos in video streams on the basis of multispectral images gradient. Instead of single frame based detection, our approach makes use of the temporal correlation of multiple consecutive frames. Since it is difficult to manually delineate logos of irregular shape, an adaptive threshold is applied to the gradient image in subpixel space to extract the logo mask. TV logo tracking is finally carried out by matching the masked region with a known template. An extensive comparison experiment has shown our proposed algorithm outperforms traditional methods such as frame difference, single frame-based edge matching. Our experimental dataset comes from part of TRECVID2005 news corpus and several Chinese TV channels with challenging TV logos

Sparse representation for robust abnormality detection in crowded scenes

In crowded scenes, the extracted low-level features, such as optical flow or spatio-temporal interest point, are inevitably noisy and uncertainty. In this paper, we propose a fully unsupervised non-negative sparse coding based approach for abnormality event detection in crowded scenes, which is specifically tailored to cope with feature noisy and uncertainty. The abnormality of query sample is decided by the sparse reconstruction cost from an atomically learned event dictionary, which forms a sparse coding bases. In our algorithm, we formulate the task of dictionary learning as a non-negative matrix factorization (NMF) problem with a sparsity constraint. We take the robust Earth Movers Distance (EMD), instead of traditional Euclidean distance, as distance metric reconstruction cost function. To reduce the computation complexity of EMD, an approximate EMD, namely wavelet EMD, is introduced and well combined into our approach, without losing performance. In addition, the combination of wavelet EMD with our approach guarantees the convexity of optimization in dictionary learning. To handle both local abnormality detection (LAD) and global abnormality detection, we adopt two different types of spatio-temporal basis. Experiments conducted on four public available datasets demonstrate the promising performance of our work against the state-of-the-art methods.

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

With the advance of digital video recording and playback systems, the request for efficiently managing recorded TV video programs is evident so that users can readily locate and browse their favorite programs. In this paper, we propose a multimodal scheme to segment and represent TV video streams. The scheme aims to recover the temporal and structural characteristics of TV programs with visual, auditory, and textual information. In terms of visual cues, we develop a novel concept named program-oriented informative images (POIM) to identify the candidate points correlated with the boundaries of individual programs. For audio cues, a multiscale Kullback-Leibler (K-L) distance is proposed to locate audio scene changes (ASC), and accordingly ASC is aligned with video scene changes to represent candidate boundaries of programs. In addition, latent semantic analysis (LSA) is adopted to calculate the textual content similarity (TCS) between shots to model the inter-program similarity and intra-program dissimilarity in terms of speech content. Finally, we fuse the multimodal features of POIM, ASC, and TCS to detect the boundaries of programs including individual commercials (spots). Towards effective program guide and attracting content browsing, we propose a multimodal representation of individual programs by using POIM images, key frames, and textual keywords in a summarization manner. Extensive experiments are carried out over an open benchmarking dataset TRECVID 2005 corpus and promising results have been achieved. Compared with the electronic program guide (EPG), our solution provides a more generic approach to determine the exact boundaries of diverse TV programs even including dramatic spots.

Collaborative Correlation Tracking

Correlation filter based tracking has attracted many researchers’ attention in recent years for high efficiency and robustness. Most existing works focus on exploiting different characteristics with correlation filters for visual tracking, e.g. circulant structure, kernel trick, effective feature representation and context information. However, how to handle the scale variation and the model drift is still an open problem. In this paper, we propose a collaborative correlation tracker to deal with the above problems. Firstly, we extend the correlation tracking filter by embedding the scale factor into the kernelized matrix to handle the scale variation. Then a novel long-term CUR filter for detection is learnt efficiently with random sampling to alleviate model drift by detecting effective object candidates in the collaborative tracker. In this way, the proposed approach could estimate the object state accurately and handle the model drift problem effectively. Extensive experiments show the superiority of the proposed method.

Consumer video retargeting: context assisted spatial-temporal grid optimization

Pervasive multimedia devices require accurate video retargeting, especially in connected consumer electronics platforms. In this paper, we present a context assisted spatialtemporal grid scheme for consumer video retargeting. First, we parse consumer videos from low-level features to highlevel visual concepts, combining visual attention into a more accurate importance description. Then, a semantic importance map is built up representing the spatial importance and temporal continuity, which is incorporated with a 3D rectilinear grid scaleplate to map frames to the target display, thereby keeping the aspect ratio of semantically salient objects as well as the perceptual coherency. Extensive evaluations were done on two popular video genres, sports and advertisements. The comparison with state-of-the-art approaches on both images and videos have demonstrated the advantages of the proposed approach.

Learning sharable models for robust background subtraction

Background modeling and subtraction is a classical topic in compute vision. Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. Lots of improvements have been made to enhance the robustness by considering spatial consistency and temporal correlation. In this paper, we propose a sharable GMM based background subtraction approach. Firstly, a sharable mechanism is presented to model the many-to-one relationship between pixels and models. Each pixel dynamically searches the best matched model in the neighborhood. This kind of space-sharing way is robust to camera jitter, dynamic background, etc. Secondly, the sharable models are built for both background and foreground. The noises resulted by local small movements could be effectively eliminated through the background sharable models, while the integrity of moving objects is enhanced by the foreground sharable models, especially for small objects. Finally, each sharable model is updated through randomly selecting a pixel which matches this model. And a flexible mechanism is added for switching between background and foreground models. Experiments on ChangeDetection benchmark dataset demonstrate the effectiveness of our approach.

Efficient clothing retrieval with semantic-preserving visual phrases

In this paper, we address the problem of large scale cross-scenario clothing retrieval with semantic-preserving visual phrases (SPVP). Since the human parts are important cues for clothing detection and segmentation, we firstly detect human parts as the semantic context, and refine the regions of human parts with sparse background reconstruction. Then, the semantic parts are encoded into the vocabulary tree under the bag-of-visual-word (BOW) framework, and the contextual constraint of visual words among different human parts is exploited through the SPVP. Moreover, the SPVP is integrated into the inverted index structure for accelerating the retrieval process. Experiments and comparisons on our clothing dataset indicate that the SPVP significantly enhances the discriminative power of local features with a slight increase of memory usage or runtime consumption compared to the BOW model. Therefore, the approach is superior to both the state-of-the-art approach and two clothing search engines.