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In this paper, a new approach for 3D skeleton-based human motion recognition is discussed. First, we opted to represent the movement as a set of body joints trajectories. Those trajectories are then converted into ropes histograms. The motion records are obtained using the Kinect motion sensor. The classification phase consists in comparing those histograms with ropes histograms of a set of reference...
In this paper, we present an approach to transcription of varnams in Carnatic music using hidden Markov models (HMMs). We draw upon domain-specific information in that we build rāga-specific HMMs and use rhythmic cycle (tā⋖a) information in the initialization of the HMMs and in the postprocessing of the output. The ground truth is obtained from standard notations of varnams. Transcription performance...
We present a novel video representation for human action recognition by considering temporal sequences of visual words. Based on state-of-the-art dense trajectories, we introduce temporal bundles of dominant, that is most frequent, visual words. These are employed to construct a complementary action representation of ordered dominant visual word sequences, that additionally incorporates fine grained...
In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation...
Sign language recognition has been the focus of research in recent years because it has enabled the use of sign languages, which are the main medium of communication for the hearing impaired, for human-computer interaction. In this work, we propose a method to recognize signs using Improved Dense Trajectory (IDT) features which were previously used in large-scale action recognition. Fisher Vectors...
Abnormal event detection plays an important role in video surveillance and smart camera systems. Existing methods in the literature are usually not object-aware, where different objects are not distinguished in processing. In this work, we propose an efficient object-aware anomaly detection scheme, specifically focusing on certain object categories, such as pedestrians. We first perform a block-based...
Noise detection in online handwritten text is an important task for data acquisition. Noise occurs in online handwritten text in various ways. For example, crossing out the previously written text due to misspelling, repeated writing of the same stroke several times following a slightly different trajectory, simply writing corrections over other text are very common. Detection of these unwanted regions...
In this paper, we propose a new system for isolated sign language recognition (SLR) and continuous SLR. In isolated SLR, Histogram of Oriented Displacement is used to describe the trajectories, and multi-SVM is adopted for classification. In continuous SLR, we propose a Dynamic Programming method with warping templates obtained by Dynamic Time Warping (DTW) algorithm. We evaluate our approach with...
Abnormal activity detection plays an important role in many areas such as surveillance, military installations, and sports. Existing abnormal activity detectors mostly rely on motion data obtained over a number of frames to characterize abnormality. However, only motion may not be able to capture all forms of abnormality, in particular, poses that do not amount to motion "outliers". In this...
Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution...
This paper presents a novel method to recognize the human gesture using binary decision tree and Multi-class Support Vector Machine (MCSVM). In a learning stage, 3D trajectory of the human gesture by a kinect sensor is assigned into the tree node of the binary decision tree according to its distribution property. The user's gesture trajectory is resampled and normalized, and we extract the chain code...
This paper proposes a framework to discover activities in an unsupervised manner, and add semantics with minimal supervision. The framework uses basic trajectory information as input and goes up to video interpretation. The work reduces the gap between low-level information and semantic interpretation, building an intermediate layer composed of Primitive Events. The proposed representation for primitive...
This paper proposes a framework to recognize and classify loosely constrained activities with minimal supervision. The framework use basic trajectory information as input and goes up to video interpretation. The work reduces the gap between low-level information and semantic interpretation, building an intermediate layer composed Primitive Events. The proposed representation for primitive events aims...
In this paper we present a novel framework for learning contextual motion model involving multiple objects in far-field surveillance video and apply the learned model to improving the performance of objects tracking and abnormal event detection. We represent trajectory of multiple objects by a 3D graph G in x,y,t, which is augmented by a number of spatio-temporal relations (links) between moving and...
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