Survey on Visual Analysis of Event Sequence Data Guo, Yi; Guo, Shunan; Jin, Zhuochen ...
IEEE transactions on visualization and computer graphics,
2022-Dec.-1, 2022-12-1, 20221201, Volume:
28, Issue:
12
Journal Article
Peer reviewed
Open access
Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, ...with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.
Outdoor acoustic event detection is an exciting research field but challenged by the need for complex algorithms and deep learning techniques, typically requiring many computational, memory, and ...energy resources. These challenges discourage IoT implementations, where an efficient use of resources is required. However, current embedded technologies and microcontrollers have increased their capabilities without penalizing energy efficiency. This paper addresses the application of sound event detection at the very edge, by optimizing deep learning techniques on resource-constrained embedded platforms for the IoT. The contribution is two-fold: firstly, a two-stage student-teacher approach is presented to make state-of-the-art neural networks for sound event detection fit on current microcontrollers; secondly, we test our approach on an ARM Cortex M4, particularly focusing on issues related to 8-bits quantization. Our embedded implementation can achieve 68% accuracy in recognition on Urbansound8k, not far from state-of-the-art performance, with an inference time of 125 ms for each second of the audio stream, and power consumption of 5.5 mW in just 34.3 kB of RAM.
Sound event detection (SED) is the task of tagging the absence or presence of audio events and their corresponding interval within a given audio clip. While SED can be done using supervised machine ...learning, where training data is fully labeled with access to per event timestamps and duration, our work focuses on weakly-supervised sound event detection (WSSED), where prior knowledge about an event's duration is unavailable. Recent research within the field focuses on improving segmentand eventlevel localization performance for specific datasets regarding specific evaluation metrics. Specifically, well-performing event-level localization requires fully labeled development subsets to obtain event duration estimates, which significantly benefits localization performance. Moreover, well-performing segment-level localization models output predictions at a coarse-scale (e.g.,1 second), hindering their deployment on datasets containing very short events (<; 1second). This work proposes a duration robust CRNN (CDur) framework, which aims to achieve competitive performance in terms of segmentand event-level localization. This paper proposes a new post-processing strategy named "Triple Threshold" and investigates two data augmentation methods along with a label smoothing method within the scope of WSSED. Evaluation of our model is done on the DCASE2017 and 2018 Task 4 datasets, and URBAN-SED. Our model outperforms other approaches on the DCASE2018 and URBAN-SED datasets without requiring prior duration knowledge. In particular, our model is capable of similar performance to strongly-labeled supervised models on the URBANSED dataset. Lastly, ablation experiments to reveal that without post-processing, our model's localization performance drop is significantly lower compared with other approaches.
Adverse event detection is critical for many real-world applications including timely identification of product defects, disasters, and major socio-political incidents. In the health context, adverse ...drug events account for countless hospitalizations and deaths annually. Since users often begin their information seeking and reporting with online searches, examination of search query logs has emerged as an important detection channel. However, search context - including query intent and heterogeneity in user behaviors - is extremely important for extracting information from search queries, and yet the challenge of measuring and analyzing these aspects has precluded their use in prior studies. We propose DeepSAVE, a novel deep learning framework for detecting adverse events based on user search query logs. DeepSAVE uses an enriched variational autoencoder encompassing a novel query embedding and user modeling module that work in concert to address the context challenge associated with search-based detection of adverse events. Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning auto encoders. Ablation analysis reveals that each component of DeepSAVE significantly contributes to its overall performance. Collectively, the results demonstrate the viability of the proposed architecture for detecting adverse events from search query logs.
The Many Shades of Negativity Zhigang Ma; Xiaojun Chang; Yi Yang ...
IEEE transactions on multimedia,
07/2017, Volume:
19, Issue:
7
Journal Article
Peer reviewed
Complex event detection has been progressively researched in recent years for the broad interest of video indexing and retrieval. To fulfill the purpose of event detection, one needs to train a ...classifier using both positive and negative examples. Current classifier training treats the negative videos as equally negative. However, we notice that many negative videos resemble the positive videos in different degrees. Intuitively, we may capture more informative cues from the negative videos if we assign them fine-grained labels, thus benefiting the classifier learning. Aiming for this, we use a statistical method on both the positive and negative examples to get the decisive attributes of a specific event. Based on these decisive attributes, we assign the fine-grained labels to negative examples to treat them differently for more effective exploitation. The resulting fine-grained labels may be not optimal to capture the discriminative cues from the negative videos. Hence, we propose to jointly optimize the fine-grained labels with the classifier learning, which brings mutual reciprocality. Meanwhile, the labels of positive examples are supposed to remain unchanged. We thus additionally introduce a constraint for this purpose. On the other hand, the state-of-the-art deep convolutional neural network features are leveraged in our approach for event detection to further boost the performance. Extensive experiments on the challenging TRECVID MED 2014 dataset have validated the efficacy of our proposed approach.
Abnormal Event Detection at 150 FPS in MATLAB Lu, Cewu; Shi, Jianping; Jia, Jiaya
2013 IEEE International Conference on Computer Vision,
12/2013
Conference Proceeding, Journal Article
Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse ...combination learning framework. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because the new method effectively turns the original complicated problem to one in which only a few costless small-scale least square optimization steps are involved. Our method reaches high detection rates on benchmark datasets at a speed of 140-150 frames per second on average when computing on an ordinary desktop PC using MATLAB.
Abnormal event detection aims to identify the events that deviate from expected normal patterns. Existing methods usually extract normal spatio-temporal patterns of appearance and motion in a ...separate manner, which ignores low-level correlations between appearance and motion patterns and may fall short of capturing fine-grained spatio-temporal patterns. In this paper, we propose to simultaneously learn appearance and motion to obtain fine-grained spatio-temporal patterns. To this end, we present an adversarial 3D convolutional auto-encoder to learn the normal spatio-temporal patterns and then identify abnormal events by diverging them from the learned normal patterns in videos. The encoder captures the low-level correlations between spatial and temporal dimensions of videos, and generates distinctive features representing visual spatio-temporal information. The decoder reconstrucccts the original video from the encoded features representing by 3D de-convolutions and learns the normal spatio-temporal patterns in an unsupervised manner. We introduce the denoising reconstruction error and adversarial learning strategy to train the 3D convolutional auto-encoder to implicitly learn accurate data distributions that are considered normal patterns, which benefits enhancing the reconstruction ability of the auto-encoder to discriminate abnormal events. Both the theoretical analysis and the extensive experiments on four publicly available datasets demonstrate the effectiveness of our method.
Zero-shot complex event detection has been an emerging task in coping with the scarcity of labeled training videos in practice. Aiming to progress beyond the state-of-the-art zero-shot event ...detection, we propose a new zero-shot event detection approach, which exploits the semantic correlation between an event and concepts. Based on the concept detectors pre-trained from external sources, our method learns the semantic correlation from the concept vocabulary and emphasizes on the most related concepts for the zero-shot event detection. Particularly, a novel Event-Adaptive Concept Integration algorithm is introduced to estimate the effectiveness of semantically related concepts by assigning different weights to them. As opposed to assigning weights by an invariable strategy, we compute the weights of concepts using the area under score curve. The assigned weights are incorporated into the confidence score vector statistically to better characterize the event-concept correlation. Our algorithm is proved to be able to harness the related concepts discriminatively tailored for a target event. Extensive experiments are conducted on the challenging TRECVID event video datasets, which demonstrate the advantage of our approach over the state-of-the-art methods.
Event detection targets at recognizing and localizing specified spatio-temporal patterns in videos. Most research of human activity recognition in the past decades experimented on relatively clean ...scenes with limited actors performing explicit actions. Recently, more efforts have been paid to the real-world surveillance videos in which the human activity recognition is more challenging due to large variations caused by factors, such as scaling, resolution, viewpoint, cluttered background, and crowdedness. In this paper, we systematically evaluate seven different types of low-level spatio-temporal features in the context of surveillance event detection (SED) using a uniform experimental setup. Fisher vector is employed to aggregate low-level features as the representation of each video clip. A set of random forests is then learned as the classification models. To bridge the research efforts and real-world applications, we utilize the NIST TRECVID SED as our testbed in which seven events are predefined involving different levels of human activity analysis. Strengths and limitations for each low-level feature type are analyzed and discussed.
Detecting events in real-time from the Twitter data stream has gained substantial attention in recent years from researchers around the world. Different event detection approaches have been proposed ...as a result of these research efforts. One of the major challenges faced in this context is the high computational cost associated with event detection in real-time. We propose, TwitterNews+, an event detection system that incorporates specialized inverted indices and an incremental clustering approach to provide a low computational cost solution to detect both major and minor newsworthy events in real-time from the Twitter data stream. In addition, we conduct an extensive parameter sensitivity analysis to fine-tune the parameters used in TwitterNews+ to achieve the best performance. Finally, we evaluate the effectiveness of our system using a publicly available corpus as a benchmark dataset. The results of the evaluation show a significant improvement in terms of recall and precision over five state-of-the-art baselines we have used.
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