The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem. We introduce a long short-term memory based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution. We also introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score and a state-based threshold. For evaluations with 1,555 robot-assisted feeding executions including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve (AUC) of 0.8710 than 5 other baseline detectors from the literature. We also show the multimodal fusion through the LSTM-VAE is effective by comparing our detector with 17 raw sensory signals versus 4 hand-engineered features.
Researchers have often reduced the dimension of high-dimensional inputs using principal component analysis (PCA) before applying probabilistic or distance-based detection @cite_0 @cite_14 . However, the compressed representations of outliers (i.e., anomalous data) may be inliers in latent space. Instead, we use a reconstruction-based method that recovers inputs from its compressed representation so that it can measure reconstruction error with the anomaly score. An AE is a representative reconstruction approach that is a connected network with an encoder and a decoder @cite_20 . It has also been applied for reconstructing time-series data using a sliding time-window @cite_15 . However, the window method does not represent dependencies between nearby windows and a window may not include an anomaly.
@cite_0 @cite_15 @cite_14 @cite_20
2108891011 2922995320 2337453828 2161041254
We present a new geometry compression method for animations, which is based on the clustered principal component analysis (CPCA). Instead of analyzing the set of vertices for each frame, our method analyzes the set of paths for all vertices for a certain animation length. Thus, using a data-driven approach, it can identify mesh parts, that are "coherent" over time. This usually leads to a very efficient and robust segmentation of the mesh into meaningful clusters, e.g. the wings of a chicken. These parts are then compressed separately using standard principal component analysis (PCA). Each of this clusters can be compressed more efficiently with lesser PCA components compared to previous approaches. Results show, that the new method outperforms other compression schemes like pure PCA based compression or combinations with linear prediction coding, while maintaining a better reconstruction error. This is true, even if the components and weights are quantized before transmission. The reconstruction process is very simple and can be performed directly on the GPU. We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unknown anomalies. Meanwhile, the unsupervised approach can detect both known and unknown anomalies that are located away from normal data points. However, it does not detect known anomalies as accurately as the supervised approach. Furthermore, even if we have labeled normal data points and anomalies, the unsupervised approach cannot utilize these labels. The ABC is a probabilistic binary classifier that effectively exploits the label information, where normal data points are modeled using the AE as a component. By maximizing the likelihood, the AE in the proposed ABC is trained to minimize the reconstruction error for normal data points, and to maximize it for known anomalies. Since our approach becomes able to reconstruct the normal data points accurately and fails to reconstruct the known and unknown anomalies, it can accurately discriminate both known and unknown anomalies from normal data points. Experimental results show that the ABC achieves higher detection performance than existing supervised and unsupervised methods. A fast and accurate video anomaly detection and localisation method is presented. The speed and localisation accuracy are two ongoing challenges in real-world anomaly detection. We introduce two novel cubic-patch-based anomaly detector where one works based on power of an auto-encoder (AE) on reconstituting an input video patch and another one is based on the power of sparse representation of an input video patch. It is found that if an AE is efficiently trained on all normal patches, the anomaly patch in testing phase has a more reconstruction error than a normal patch. Also if a sparse AE is learned based on normal training patches, we expect that the given patch to AE is represented sparsely. If the representation is not enough sparse it is considered as a good candidate to be anomaly. For being more fast, these two detectors are combined as a cascade classifier. First, all small patches on test video frame are scanned, those which have not enough sparse representation are resized and sent to next detector for more careful evaluation. The experiment results show that the method mentioned here has a better performance especially in run-time measure than state-of-the-art methods on two UMN and UCSD benchmarks. Mining useful clusters from high dimensional data have received significant attention of the computer vision and pattern recognition community in the recent years. Linear and nonlinear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), nonconvexity (for matrix factorization methods), and susceptibility to gross corruptions in the data. In this paper, we propose a principal component analysis (PCA) based solution that overcomes these three issues and approximates a low-rank recovery method for high dimensional datasets. We target the low-rank recovery by enforcing two types of graph smoothness assumptions, one on the data samples and the other on the features by designing a convex optimization problem. The resulting algorithm is fast, efficient, and scalable for huge datasets with O(n log(n)) computational complexity in the number of data samples. It is also robust to gross corruptions in the dataset as well as to the model parameters. Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models. Our theoretical analysis proves that the proposed model is able to recover approximate low-rank representations with a bounded error for clusterable data.