Move Forward and Tell: A Progressive Generator of Video Descriptions

We present an efficient framework that can generate a coherent paragraph to describe a given video. Previous works on video captioning usually focus on video clips. They typically treat an entire video as a whole and generate the caption conditioned on a single embedding. On the contrary, we consider videos with rich temporal structures and aim to generate paragraph descriptions that can preserve the story flow while being coherent and concise. Towards this goal, we propose a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions. Given a video, it selects a sequence of distinctive clips and generates sentences thereon in a coherent manner. Particularly, the selection of clips and the production of sentences are done jointly and progressively driven by a recurrent network -- what to describe next depends on what have been said before. Here, the recurrent network is learned via self-critical sequence training with both sentence-level and paragraph-level rewards. On the ActivityNet Captions dataset, our method demonstrated the capability of generating high-quality paragraph descriptions for videos. Compared to those by other methods, the descriptions produced by our method are often more relevant, more coherent, and more concise.Comment: Accepted by ECCV 201

Towards Structured Analysis of Broadcast Badminton Videos

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score ([email protected]), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.Comment: 9 page

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning

Video paragraph captioning aims to generate a multi-sentence description of an untrimmed video with several temporal event locations in coherent storytelling. Following the human perception process, where the scene is effectively understood by decomposing it into visual (e.g. human, animal) and non-visual components (e.g. action, relations) under the mutual influence of vision and language, we first propose a visual-linguistic (VL) feature. In the proposed VL feature, the scene is modeled by three modalities including (i) a global visual environment; (ii) local visual main agents; (iii) linguistic scene elements. We then introduce an autoregressive Transformer-in-Transformer (TinT) to simultaneously capture the semantic coherence of intra- and inter-event contents within a video. Finally, we present a new VL contrastive loss function to guarantee learnt embedding features are matched with the captions semantics. Comprehensive experiments and extensive ablation studies on ActivityNet Captions and YouCookII datasets show that the proposed Visual-Linguistic Transformer-in-Transform (VLTinT) outperforms prior state-of-the-art methods on accuracy and diversity.Comment: Accepted to AAAI 202

Towards Multi-modal Explainable Video Understanding

This thesis presents a novel approach to video understanding by emulating human perceptual processes and creating an explainable and coherent storytelling representation of video content. Central to this approach is the development of a Visual-Linguistic (VL) feature for an interpretable video representation and the creation of a Transformer-in-Transformer (TinT) decoder for modeling intra- and inter-event coherence in a video. Drawing inspiration from the way humans comprehend scenes by breaking them down into visual and non-visual components, the proposed VL feature models a scene through three distinct modalities. These include: (i) a global visual environment, providing a broad contextual understanding of the scene; (ii) local visual main agents, focusing on key elements or entities in the video; and (iii) linguistic scene elements, incorporating semantically relevant language-based information for a comprehensive understanding of the scene. By integrating these multimodal features, the VL representation offers a rich, diverse, and interpretable view of video content, effectively bridging the gap between visual perception and linguistic description. To ensure the temporal coherence and narrative structure of the video content, we introduce an autoregressive Transformer-in-Transformer (TinT) decoder. The TinT design consists of a nested architecture where the inner transformer models the intra-event coherency, capturing the semantic connections within individual events, while the outer transformer models the inter-event coherency, identifying the relationships and transitions between different events. This dual-layer transformer structure facilitates the generation of accurate and meaningful video descriptions that reflect the chronological and causal links in the video content. Another crucial aspect of this work is the introduction of a novel VL contrastive loss function. This function plays an essential role in ensuring that the learned embedding features are semantically consistent with the video captions. By aligning the embeddings with the ground truth captions, the VL contrastive loss function enhances the model\u27s performance and contributes to the quality of the generated descriptions. The efficacy of our proposed methods is validated through comprehensive experiments on popular video understanding benchmarks. The results demonstrate superior performance in terms of both the accuracy and diversity of the generated captions, highlighting the potential of our approach in advancing the field of video understanding. In conclusion, this thesis provides a promising pathway toward building explainable video understanding models. By emulating human perception processes, leveraging multimodal features, and incorporating a nested transformer design, we contribute a new perspective to the field, paving the way for more advanced and intuitive video understanding systems in the future

Towards Multi-modal Explainable Video Understanding

This thesis presents a novel approach to video understanding by emulating human perceptual processes and creating an explainable and coherent storytelling representation of video content. Central to this approach is the development of a Visual-Linguistic (VL) feature for an interpretable video representation and the creation of a Transformer-in-Transformer (TinT) decoder for modeling intra- and inter-event coherence in a video. Drawing inspiration from the way humans comprehend scenes by breaking them down into visual and non-visual components, the proposed VL feature models a scene through three distinct modalities. These include: (i) a global visual environment, providing a broad contextual understanding of the scene; (ii) local visual main agents, focusing on key elements or entities in the video; and (iii) linguistic scene elements, incorporating semantically relevant language-based information for a comprehensive understanding of the scene. By integrating these multimodal features, the VL representation offers a rich, diverse, and interpretable view of video content, effectively bridging the gap between visual perception and linguistic description. To ensure the temporal coherence and narrative structure of the video content, we introduce an autoregressive Transformer-in-Transformer (TinT) decoder. The TinT design consists of a nested architecture where the inner transformer models the intra-event coherency, capturing the semantic connections within individual events, while the outer transformer models the inter-event coherency, identifying the relationships and transitions between different events. This dual-layer transformer structure facilitates the generation of accurate and meaningful video descriptions that reflect the chronological and causal links in the video content. Another crucial aspect of this work is the introduction of a novel VL contrastive loss function. This function plays an essential role in ensuring that the learned embedding features are semantically consistent with the video captions. By aligning the embeddings with the ground truth captions, the VL contrastive loss function enhances the model\u27s performance and contributes to the quality of the generated descriptions. The efficacy of our proposed methods is validated through comprehensive experiments on popular video understanding benchmarks. The results demonstrate superior performance in terms of both the accuracy and diversity of the generated captions, highlighting the potential of our approach in advancing the field of video understanding. In conclusion, this thesis provides a promising pathway toward building explainable video understanding models. By emulating human perception processes, leveraging multimodal features, and incorporating a nested transformer design, we contribute a new perspective to the field, paving the way for more advanced and intuitive video understanding systems in the future

Automatic video segmentation employing object/camera modeling techniques

Practically established video compression and storage techniques still process video sequences as rectangular images without further semantic structure. However, humans watching a video sequence immediately recognize acting objects as semantic units. This semantic object separation is currently not reflected in the technical system, making it difficult to manipulate the video at the object level. The realization of object-based manipulation will introduce many new possibilities for working with videos like composing new scenes from pre-existing video objects or enabling user-interaction with the scene. Moreover, object-based video compression, as defined in the MPEG-4 standard, can provide high compression ratios because the foreground objects can be sent independently from the background. In the case that the scene background is static, the background views can even be combined into a large panoramic sprite image, from which the current camera view is extracted. This results in a higher compression ratio since the sprite image for each scene only has to be sent once. A prerequisite for employing object-based video processing is automatic (or at least user-assisted semi-automatic) segmentation of the input video into semantic units, the video objects. This segmentation is a difficult problem because the computer does not have the vast amount of pre-knowledge that humans subconsciously use for object detection. Thus, even the simple definition of the desired output of a segmentation system is difficult. The subject of this thesis is to provide algorithms for segmentation that are applicable to common video material and that are computationally efficient. The thesis is conceptually separated into three parts. In Part I, an automatic segmentation system for general video content is described in detail. Part II introduces object models as a tool to incorporate userdefined knowledge about the objects to be extracted into the segmentation process. Part III concentrates on the modeling of camera motion in order to relate the observed camera motion to real-world camera parameters. The segmentation system that is described in Part I is based on a background-subtraction technique. The pure background image that is required for this technique is synthesized from the input video itself. Sequences that contain rotational camera motion can also be processed since the camera motion is estimated and the input images are aligned into a panoramic scene-background. This approach is fully compatible to the MPEG-4 video-encoding framework, such that the segmentation system can be easily combined with an object-based MPEG-4 video codec. After an introduction to the theory of projective geometry in Chapter 2, which is required for the derivation of camera-motion models, the estimation of camera motion is discussed in Chapters 3 and 4. It is important that the camera-motion estimation is not influenced by foreground object motion. At the same time, the estimation should provide accurate motion parameters such that all input frames can be combined seamlessly into a background image. The core motion estimation is based on a feature-based approach where the motion parameters are determined with a robust-estimation algorithm (RANSAC) in order to distinguish the camera motion from simultaneously visible object motion. Our experiments showed that the robustness of the original RANSAC algorithm in practice does not reach the theoretically predicted performance. An analysis of the problem has revealed that this is caused by numerical instabilities that can be significantly reduced by a modification that we describe in Chapter 4. The synthetization of static-background images is discussed in Chapter 5. In particular, we present a new algorithm for the removal of the foreground objects from the background image such that a pure scene background remains. The proposed algorithm is optimized to synthesize the background even for difficult scenes in which the background is only visible for short periods of time. The problem is solved by clustering the image content for each region over time, such that each cluster comprises static content. Furthermore, it is exploited that the times, in which foreground objects appear in an image region, are similar to the corresponding times of neighboring image areas. The reconstructed background could be used directly as the sprite image in an MPEG-4 video coder. However, we have discovered that the counterintuitive approach of splitting the background into several independent parts can reduce the overall amount of data. In the case of general camera motion, the construction of a single sprite image is even impossible. In Chapter 6, a multi-sprite partitioning algorithm is presented, which separates the video sequence into a number of segments, for which independent sprites are synthesized. The partitioning is computed in such a way that the total area of the resulting sprites is minimized, while simultaneously satisfying additional constraints. These include a limited sprite-buffer size at the decoder, and the restriction that the image resolution in the sprite should never fall below the input-image resolution. The described multisprite approach is fully compatible to the MPEG-4 standard, but provides three advantages. First, any arbitrary rotational camera motion can be processed. Second, the coding-cost for transmitting the sprite images is lower, and finally, the quality of the decoded sprite images is better than in previously proposed sprite-generation algorithms. Segmentation masks for the foreground objects are computed with a change-detection algorithm that compares the pure background image with the input images. A special effect that occurs in the change detection is the problem of image misregistration. Since the change detection compares co-located image pixels in the camera-motion compensated images, a small error in the motion estimation can introduce segmentation errors because non-corresponding pixels are compared. We approach this problem in Chapter 7 by integrating risk-maps into the segmentation algorithm that identify pixels for which misregistration would probably result in errors. For these image areas, the change-detection algorithm is modified to disregard the difference values for the pixels marked in the risk-map. This modification significantly reduces the number of false object detections in fine-textured image areas. The algorithmic building-blocks described above can be combined into a segmentation system in various ways, depending on whether camera motion has to be considered or whether real-time execution is required. These different systems and example applications are discussed in Chapter 8. Part II of the thesis extends the described segmentation system to consider object models in the analysis. Object models allow the user to specify which objects should be extracted from the video. In Chapters 9 and 10, a graph-based object model is presented in which the features of the main object regions are summarized in the graph nodes, and the spatial relations between these regions are expressed with the graph edges. The segmentation algorithm is extended by an object-detection algorithm that searches the input image for the user-defined object model. We provide two objectdetection algorithms. The first one is specific for cartoon sequences and uses an efficient sub-graph matching algorithm, whereas the second processes natural video sequences. With the object-model extension, the segmentation system can be controlled to extract individual objects, even if the input sequence comprises many objects. Chapter 11 proposes an alternative approach to incorporate object models into a segmentation algorithm. The chapter describes a semi-automatic segmentation algorithm, in which the user coarsely marks the object and the computer refines this to the exact object boundary. Afterwards, the object is tracked automatically through the sequence. In this algorithm, the object model is defined as the texture along the object contour. This texture is extracted in the first frame and then used during the object tracking to localize the original object. The core of the algorithm uses a graph representation of the image and a newly developed algorithm for computing shortest circular-paths in planar graphs. The proposed algorithm is faster than the currently known algorithms for this problem, and it can also be applied to many alternative problems like shape matching. Part III of the thesis elaborates on different techniques to derive information about the physical 3-D world from the camera motion. In the segmentation system, we employ camera-motion estimation, but the obtained parameters have no direct physical meaning. Chapter 12 discusses an extension to the camera-motion estimation to factorize the motion parameters into physically meaningful parameters (rotation angles, focal-length) using camera autocalibration techniques. The speciality of the algorithm is that it can process camera motion that spans several sprites by employing the above multi-sprite technique. Consequently, the algorithm can be applied to arbitrary rotational camera motion. For the analysis of video sequences, it is often required to determine and follow the position of the objects. Clearly, the object position in image coordinates provides little information if the viewing direction of the camera is not known. Chapter 13 provides a new algorithm to deduce the transformation between the image coordinates and the real-world coordinates for the special application of sport-video analysis. In sport videos, the camera view can be derived from markings on the playing field. For this reason, we employ a model of the playing field that describes the arrangement of lines. After detecting significant lines in the input image, a combinatorial search is carried out to establish correspondences between lines in the input image and lines in the model. The algorithm requires no information about the specific color of the playing field and it is very robust to occlusions or poor lighting conditions. Moreover, the algorithm is generic in the sense that it can be applied to any type of sport by simply exchanging the model of the playing field. In Chapter 14, we again consider panoramic background images and particularly focus ib their visualization. Apart from the planar backgroundsprites discussed previously, a frequently-used visualization technique for panoramic images are projections onto a cylinder surface which is unwrapped into a rectangular image. However, the disadvantage of this approach is that the viewer has no good orientation in the panoramic image because he looks into all directions at the same time. In order to provide a more intuitive presentation of wide-angle views, we have developed a visualization technique specialized for the case of indoor environments. We present an algorithm to determine the 3-D shape of the room in which the image was captured, or, more generally, to compute a complete floor plan if several panoramic images captured in each of the rooms are provided. Based on the obtained 3-D geometry, a graphical model of the rooms is constructed, where the walls are displayed with textures that are extracted from the panoramic images. This representation enables to conduct virtual walk-throughs in the reconstructed room and therefore, provides a better orientation for the user. Summarizing, we can conclude that all segmentation techniques employ some definition of foreground objects. These definitions are either explicit, using object models like in Part II of this thesis, or they are implicitly defined like in the background synthetization in Part I. The results of this thesis show that implicit descriptions, which extract their definition from video content, work well when the sequence is long enough to extract this information reliably. However, high-level semantics are difficult to integrate into the segmentation approaches that are based on implicit models. Intead, those semantics should be added as postprocessing steps. On the other hand, explicit object models apply semantic pre-knowledge at early stages of the segmentation. Moreover, they can be applied to short video sequences or even still pictures since no background model has to be extracted from the video. The definition of a general object-modeling technique that is widely applicable and that also enables an accurate segmentation remains an important yet challenging problem for further research

Language-Driven Video Understanding

Video understanding has advanced quite a long way in the past decade, accomplishing tasks including low-level segmentation and tracking that study objects as pixel-level segments or bounding boxes to more high-level activity recognition or classification tasks that classify a video scene to a categorical action label. Despite the progress that has been made, much of this work remains a proxy for an eventual task or application that requires a holistic view of the video, such as objects, actions, attributes, and other semantic components. In this dissertation, we argue that language could deliver the required holistic representation. It plays a significant role in video understanding by allowing machines to communicate with humans and to understand our requests, as shown in tasks such as text-to-video search engine, voice-guided robot manipulation, to name a few. Our language-driven video understanding focuses on two specific problems: video description and visual grounding. What marks our viewpoint different from prior literature is twofold. First, we propose a bottom-up structured learning scheme by decomposing a long video into individual procedure steps and representing each step with a description. Second, we propose to have both explicit (i.e., supervised) and implicit (i.e., weakly-supervised and self-supervised) grounding between words and visual concepts which enables interpretable modeling of the two spaces. We start by drawing attention to the shortage of large benchmarks on long video-language and propose the largest-of-its-kind YouCook2 dataset and ActivityNet-Entities dataset in Chap. II and III. The rest of the chapters circle around two main problems: video description and visual grounding. For video description, we first address the problem of decomposing a long video into compact and self-contained event segments in Chap. IV. Given an event segment or short video clip in general, we propose a non-recurrent approach (i.e., Transformer) for video description generation in Chap. V as opposed to prior RNN-based methods and demonstrate superior performance. Moving forward, we notice one potential issue in end-to-end video description generation, i.e., lack of visual grounding ability and model interpretability that would allow humans to directly interact with machine vision models. To address this issue, we transition our focus from end-to-end, video-to-text systems to systems that could explicitly capture the grounding between the two modalities, with a novel grounded video description framework in Chap. VI. So far, all the methods are fully-supervised, i.e., the model training signal comes directly from heavy & expensive human annotations. In the following chapter, we answer the question "Can we perform visual grounding without explicit supervision?" with a weakly-supervised framework where models learn grounding from (weak) description signal. Finally, in Chap. VIII, we conclude the technical work by exploring a self-supervised grounding approach—vision-language pre-training—that implicitly learns visual grounding from web multi-modal data. This mimics how humans obtain their commonsense from the environment through multi-modal interactions.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155174/1/luozhou_1.pd

Content-based video indexing for sports applications using integrated multi-modal approach

This thesis presents a research work based on an integrated multi-modal approach for sports video indexing and retrieval. By combining specific features extractable from multiple (audio-visual) modalities, generic structure and specific events can be detected and classified. During browsing and retrieval, users will benefit from the integration of high-level semantic and some descriptive mid-level features such as whistle and close-up view of player(s). The main objective is to contribute to the three major components of sports video indexing systems. The first component is a set of powerful techniques to extract audio-visual features and semantic contents automatically. The main purposes are to reduce manual annotations and to summarize the lengthy contents into a compact, meaningful and more enjoyable presentation. The second component is an expressive and flexible indexing technique that supports gradual index construction. Indexing scheme is essential to determine the methods by which users can access a video database. The third and last component is a query language that can generate dynamic video summaries for smart browsing and support user-oriented retrievals