Quantifying and Detecting Collective Motion in Crowd Scenes

Li, Xuelong; Chen, Mulin; Wang, Qi

doi:10.1109/tip.2020.2985284

Cited by 33 publications

(14 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, we compare the classification performance of the proposed HAD-based approach with the state-of-the-art approaches [16], [24], [10], which reports only binary classification results for the pair-wise combinations of structured vs. structured, structured vs. semi-structured, and semistructured vs. unstructured scenes. The results shown in Table 2 (results for the existing approaches are obtained from the paper [10]) clearly indicates the superior performance of the proposed HAD-based approach, which is also able to perform consistently well for each of the selected three classifiers. The state-of-the-art approaches work by complex interactions among the trajectories to generate a collectiveness measure.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, Li et al [25] used a point selection strategy to refine the features points which are tracked, followed by a manifold ranking approach to compute the crowd collectiveness. In an extended work, Li et al [10,26] modelled the motion intention of individuals in a scene by proposing an intention-aware model which is combined with a manifold ranking strategy to compute the collectiveness of the crowd. Recently, Roy et al [27] proposed an approach to classify a given crowded scene into structured, semi-structured and unstructured based on the definitions presented in [15].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

et al. 2022

View full text Add to dashboard Cite

Automated crowd behaviour analysis and monitoring is a challenging task due to the unpredictable nature of the crowd within a particular scene and across different scenes. The prior knowledge of the type of scene under consideration is a crucial mid-level information, which could be utilized to develop robust crowd behaviour analysis systems. In this paper, we propose an approach to automatically detect the type of a crowded scene based on the global motion patterns of the objects within the scene. Three different types of scenes whose global motion pattern characteristics vary from uniform to non-uniform are considered in this work, namely structured, semi-structured, and unstructured scenes, respectively. To capture the global motion pattern characteristics of an input crowd scene, we first extract the motion information in the form of trajectories using a key-point tracker and then compute the average angular orientation feature of each trajectory. This paper utilizes these angular features to introduce a novel feature vector, termed as Histogram of Angular Deviations (HAD), which depicts the distribution of the pair-wise angular deviation values for each trajectory vector. Since angular deviation information is resistant to changes in scene perspectives, we consider it as a key feature for distinguishing the scene types. To evaluate the effectiveness of the proposed HAD-based feature vector in classifying the crowded scenes, we build a crowd scene classification model by training the classical machine learning algorithms on the publicly available Collective Motion Database. The experimental results demonstrate the superior crowd classification performance of the proposed approach as compared to the existing methods. In addition to this, we propose a technique based on quantizing the angular deviation values to reduce the feature dimension and subsequently introduce a novel crowd scene structuredness index to quantify the structuredness of an input crowded scene based on its HAD.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Crowd analysis is still present in papers in 2020. Li et al [58] propose quantifying and detecting collective motion through a multi-stage clustering strategy and a method to provide crowd anomaly detection and localization using histogram of magnitude and momentum [4]. Regarding crowds and environments: Sun et al [95] propose to predict crowd flows using graph convolutional networks.…”

Section: New Trends: Papers From 2020 and Beyondmentioning

confidence: 99%

A history of crowd simulation: the past, evolution, and new perspectives

2021

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning models have improved the state of the art related to recognizing speech or objects, image processing, signal and information [12,21,23]. For the problem of detection and classification of PQ disturbances, deep neural networks can not only improve the performance, but also automate the process of extraction and selection of features, reducing and simplifying the process, from a threestep process into one unified process.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for the Detection and Classification of Power Quality Disturbances with Windowed Signals

et al. 2021

View full text Add to dashboard Cite

The modernization of Power Systems (PSs) to smart grids, the expansion of microgrids, the ever-increasing presence of distributed power generation, the more frequent use of non-linear and voltage-sensitive loads by the consumers have caused problems to the Power Quality (PQ). The studies in PQ are commonly related to disturbances that alter the sinusoidal characteristics of the voltage waveforms and/or current. The first step to analyzing the PQ is to detect and then classify the disturbances, since by identifying the disturbance, it is possible to know its causes and deliberate over strategies to mitigate it. Thus, this paper proposes a deep-learning approach using voltage signals, without pre-processing, extraction, nor manual selection of features in order to detect and classify PQ disturbances automatically. The proposed approach is composed of convolution layers, a pooling layer, a long short-term memory layer, and batch normalization. A 1D convolution was used to adapt the data from the voltage signals. Overlapping windowed signals with different Signal-Noise Ratio (SNR) (40 dB, 30 dB, 20 dB and 0 dB) and with different sampling rates (16, 32, and 64 samples/cycle) were used. For a more in-depth view of the results, the proposed approach was evaluated for its accuracy, precision, recall, and F1-Score in different scenarios. An analysis of the obtained results shows that even for the worst case scenario (SNR of 20 dB and sampling rates of 16 samples/ cycle), the approach performs satisfactorily with values above 0.97 for the analyzed metrics, allowing, thus, consumer action in a demand-side management scenario.

show abstract

Quantifying and Detecting Collective Motion in Crowd Scenes

Cited by 33 publications

References 49 publications

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

A history of crowd simulation: the past, evolution, and new perspectives

A Deep Learning Approach for the Detection and Classification of Power Quality Disturbances with Windowed Signals

Contact Info

Product

Resources

About