Anomalous Behaviors Detection in Moving Crowds Based on a Weighted Convolutional Autoencoder-Long Short-Term Memory Network

Yang, Biao; Cao, Jinmeng; Wang, Nan; Liu, Xiaofeng

doi:10.1109/tcds.2018.2866838

Cited by 37 publications

(11 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rise of deep learning [13,14], many data-driven methods have been proposed in recent years since trajectory prediction could be regarded as a sequence classification or a sequence generation task. LSTM network [15] is widely used for information mining and deep representation when dealing with time-series data.…”

Section: Figure 1: Driving Scenario Of An Autonomous Vehiclementioning

confidence: 99%

Forecasting Future Trajectories with an Improved Transformer Network

Wu¹,

Zhang²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

An increase in car ownership brings convenience to people's life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents' future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents' future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. To perform trajectory prediction more efficiently, a probabilistic sparse self-attention mechanism is introduced to reduce attention complexity by reducing the number of queried values in the attention mechanism. Activate or not (ACON) activation function is adopted to select whether to activate or not, hence improving model flexibility. The proposed method is evaluated on the publicly available benchmarks nextgeneration simulation (NGSIM) and ETH/UCY. The experimental results indicate that the proposed method can accurately and efficiently predict agents' trajectories.

show abstract

Section: Figure 1: Driving Scenario Of An Autonomous Vehiclementioning

confidence: 99%

Forecasting Future Trajectories with an Improved Transformer Network

Wu¹,

Zhang²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…According to the way of eye movement, two terms "Fixation" and "Saccade" can be used to describe a series of eye movements [20]. It is defined as follows.…”

Section: Eye Movement Analysis Algorithmmentioning

confidence: 99%

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

By reversing the arrangement of knowledge transfer and knowledge internalisation, FC (flipped classroom) changed the roles of teachers and students in traditional teaching and replanned the use of classroom time, realizing the innovation of traditional teaching mode. This research uses a network teaching platform to create a network platform supporting environment suitable for FC teaching in universities, allowing for the reform of university classroom teaching modes. An LSTM (Long Short-Term Memory) eye movement analysis algorithm is proposed to solve this problem. By analyzing the sequence of eye movements, this algorithm can predict students’ learning behavior and complete the current learning state detection. The research shows that the design and application of this research have achieved a good teaching effect in practice, which provides an idea for flipped classroom teaching design and curriculum resource design and development.

show abstract

“…All these models get easily influenced by complex backgrounds as these approaches detect anomalies mainly derived from reconstruction errors of the entire frame. The author of Reference 19 introduced convolutional auto‐encoder (CAE) based fault identification by capturing temporal information using long short term memory (LSTM) and capable of finding small region anomaly using generative adversarial networks (GAN) based method. Abnormal behavior detection in crowd scenes has important application in the computer vision field.…”

Section: Related Workmentioning

confidence: 99%

Abnormal behavior detection of stationary objects in surveillance videos with visualization and classification

Sharma

Gangadharappa

2022

Concurrency and Computation

View full text Add to dashboard Cite

Anomaly detection in video systems has been popular over several years. It is still challenging to detect anomalies in a static object. To manage this objective, we focus on changes in the position of a stationary object in videos. In a normal scenario, the pixel values of the static object are fixed while in abnormal motion the fixed values change.We introduce a new concept to determine anomalies based on manual annotations in each video frame, only over a part of a static object in a frame such that it can be taken as a reference for the whole. Through color channel splitting we determine mask image, from which handcrafted features such as scratch area, perimeter, equivalent diameter and density are calculated. In the next step, we analyze frame-wise changes in feature values using a linear regression model, feature values are constant when the object remains stationary while there is a rise or fall in values when an object changes location. We classify feature values through anomaly scores and thresholds. In this model, we are evaluating our proposed framework on 12 real-time video datasets. Results are compared with existing techniques which are outperforming in terms of accuracy, mean square error and area under the curve.

show abstract

Anomalous Behaviors Detection in Moving Crowds Based on a Weighted Convolutional Autoencoder-Long Short-Term Memory Network

Cited by 37 publications

References 31 publications

Forecasting Future Trajectories with an Improved Transformer Network

Forecasting Future Trajectories with an Improved Transformer Network

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

Abnormal behavior detection of stationary objects in surveillance videos with visualization and classification

Contact Info

Product

Resources

About