Motion Recognition Based on Sum of the Squared Errors Distribution

Abstract

Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this paper has better recognition performance.

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

“…Non-data-adaptive methods involve analyzing and predicting data based on predetermined rules and parameters without any kind of data adjustment or training. Spectrum analysis is a common non-data-adaptive representation approach [28,29]. For example, Agrawal et al [28] proposed a feature representation approach based on the Discrete Fourier Transform, which maps time series data to the frequency domain and then performs similarity queries using R*-trees.…”

“…Liu et al [30] utilized the Discrete Wavelet Transform to jointly represent the time-frequency domain information of data. In addition, some other non-data-adaptive methodologies in [29,31,32] have also been proposed for the analysis of motion sequence. Keogh et al [33] proposed a method based on segment-by-segment linear segmentation for determining the shape of the sequences.…”

“…Similarly, Chatzigeorgakidis et al [31] adopted a piecewise aggregate approximation to extract the representative features from the sequence data, which divides the original data into segments and then calculates the average value of each segment as a representative feature. Overall, non-dataadaptive methods are fast and simple due to their minimal reliance on training data and model parameter adjustments, but their strong dependence on a priori assumptions can often lead to underfitting or high error classification results, especially when dealing with complex motion sequences [29].…”

Sports Motion Feature Extraction and Recognition Based on a Modified Histogram of Oriented Gradients with Speeded Up Robust Features

“…For other obvious features, the recognition rate is higher and the misrecognition rate is lower in this paper. In order to evaluate the performance of the proposed algorithm in this paper, the recognition accuracy rate (RAA) is taken as the index to compare with other algorithms SSED [17], MCSM-Wri [18], PGCN-TCA [19], AR3D [20]. The comparison results are shown in Table 1.…”