Coding local and global binary visual features extracted from video sequences

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin

Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked $3^{rd}$ place in this challenge

Coding binary local features extracted from video sequences

Local features represent a powerful tool which is exploited in several applications such as visual search, object recognition and tracking, etc. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to low computational complexity, limited memory footprint and fast matching algorithms. The descriptor consists of a binary vector, in which each bit is the result of a pairwise comparison between smoothed pixel intensities. In several cases, visual features need to be transmitted over a bandwidth-limited network. To this end, it is useful to compress the descriptor to reduce the required rate, while attaining a target accuracy for the task at hand. The past literature thoroughly addressed the problem of coding visual features extracted from still images and, only very recently, the problem of coding real-valued features (e.g., SIFT, SURF) extracted from video sequences. In this paper we propose a coding architecture specifically designed for binary local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes, showing that significant coding gains can be attained for a target level of accuracy of the visual analysis task

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Interaction between high-level and low-level image analysis for semantic video object extraction

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