Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements.

Labanotation is a professional dance notation system widely used in dance education and choreography preservation. Automatically generatingLabanotation dance scores from motion capture data can save a huge amount of manual time and effort. Recently, the sequence-to-sequence (seq2seq) model is applied to the automatic Labanotation generation. This model is based on an encoder-decoder structure, which encodes the input motion sequence to a fixed-length vector and then decodes it to generate the target sequence. However, the encoding of spatial skeleton structure of motion data is not considered in the existing work. Besides, it is challenging to align between the input motion data and the output Laban symbol sequences due to the severe imbalance of sequence lengths. Therefore, in this paper, we present a new seq2seq model for more effective Labanotation generation. In the encoder, we propose a new gesture-sensitive graph convolutional network with learned adaptive joint weights and non-physical connections to learn both spatial and temporal patterns from motion data sequences. In the decoder, we exploit motion rhythm information and propose a novel rhythm-aware attention mechanism to learn a good alignment between motion sequences and Laban symbol sequences, so that we can focus on relevant parts of the input motion sequence without searching in the whole sequence when predicting a target Laban symbol. Extensive experiments on two real-world datasets show that the proposed method achieves a better performance compared with the state-of-the-art approaches on the task of automatic Labanotation generation.

Labanotation is an important notation system widely used for recording dances. Numerous methods have been proposed for automatic Labanotation generation from motion capture data. Recently, the sequence-to-sequence (seq2seq) model is proposed. However, the encoder of the model only encodes the temporal information of motion data, lacking the encoding for spatial information. And it is challenging for the decoder to align input and output sequences due to the imbalance of the sequence lengths. In this paper, we propose an attention-seq2seq model based on Convolutional Recurrent Neural Network (CRNN). The proposed model employs an encoder based on CRNN to learn the spatial-temporal information of motion data and applies an attention mechanism to align each target Laban symbol with relevant parts of the input motion data in decoding. Experiments show that the proposed method performs favorably against state-of-the-art algorithms in the automatic Labanotation generation task.

Labanotation is an important notation system for recording dances. Automatically generating Labanotation scores from motion capture data has attracted more interest in recent years. Current methods usually focus on individual movement segments and generate Labanotation symbols one by one. This requires segmenting the captured data sequence in advance. Manual segmentation will consume a lot of time and effort, while automatic segmentation may not be reliable enough. In this paper, we propose a sequence-to-sequence approach that can generate Labanotation scores from unsegmented motion data sequences. First, we extract effective features from motion capture data based on body skeleton analysis. Then, we train a neural network under the encoder-decoder architecture to transform the motion feature sequences to corresponding Labanotation symbols. As such, the dance score is generated. Experiments show that the proposed method performs favorably against state-of-the-art algorithms in the automatic Labanotation generation task.

Labanotation is a widely used dance notation system. The topic of generating Labanotation scores from captured dance motion data has attracted more research interest in recent years. Current methods usually generate Laban symbols via recognizing motion segments that each contains a single dance movement. However, they rely on the manual segmentation of raw dance motion sequences, which can cost a lot of time and effort. In this paper, we present a fully automatic framework to generate Labanotation scores from continuous motion data. First, we split the captured dance data to motion segments based on the Laban theory of body weight support transferring. Then, based on the segmented data, we utilize a network with both 1D-convolutional and recurrent layers to recognize body movements and generate Laban symbols. As such, the Labanotation score is created. Extensive experiments show that the proposed automatic framework performs favorably against previous solutions.

Real-time object tracking has wide applications in time-critical multimedia processing areas such as motion analysis and human-computer interaction. It remains a hard problem to balance between accuracy and speed. In this paper, we present a fast real-time context-based visual tracking algorithm with a new saliency prior context (SPC) model. Based on the probability formulation, the tracking problem is solved by sequentially maximizing the computed confidence map of target location in each video frame. To handle the various cases of feature distributions generated from different targets and their contexts, we exploit low-level features as well as fast spectral analysis for saliency to build a new prior context model. Then, based on this model and a spatial context model learned online, a confidence map is computed and the target location is estimated. In addition, under this framework, the tracking procedure can be accelerated by the fast Fourier transform. Therefore, the new method generally achieves a real-time running speed. Extensive experiments show that our tracking algorithm based on the proposed SPC model achieves real-time computation efficiency with overall best performance comparing with other state-of-the-art methods.

In this paper, we propose a preprocessing strategy for refining the pedestrian images in person re-identification(re-id). The accomplishment of the person re-id task depends on the features extracted from pedestrian appearances. The best image matches are verified based on these features as identification results. The saliency information in image scenes is often exploited for feature selection in high-level vision tasks. Inspired by the pre-attentive mechanism in human visual system, we utilize the saliency information in the re-identification data to refine the person appearance in a preprocessing step. We first obtain the eye-fixation-predicting map based on the saliency analysis of image. Then this map is used to spatially weight the image features for a better appearance. Finally, we apply these processed images to the feature extraction in a standard re-identification procedure. Experiments on the widely-used VIPeR dataset show that the proposed method improves the final performance of re-identification task.