Objective

2

Collective motion detection is fundamentally important for analyzing crowd behavior and has attracted considerable attention in artificial intelligence. The recognition and segmentation of collective behavior are important branches of computer vision and graphics

which are significant for public safety

intelligent traffic

and architectural design. The main task of recognizing collective behavior is to mine the coherent patterns consisting of highly coherent tracklets according to the extracted features from crowd motion in videos. However

existing works mostly have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. Collective behavior involves local and global motion patterns

in which varying densities and arbitrary shapes are salient characteristics. The global coherent motion with complex interaction requires an accurate measurement of local coherency and analysis of global continuity. A further study demonstrates that the motion descriptor and similarity measurement remain limited to finding the latent relativity among tracklet points under the circumstances of perspective distortion and large spatial gap. In view of these shortcomings

we propose the density-based manifold collective clustering approach for detecting groups in crowd scenes.

Method

2

Our background modeling is based on the clustering strategy and aims to recognize arbitrarily shaped clusters in coherent motion. Motion features are detected with the generalized Kanade-Lucas-Tomasi feature point tracker

which jointly combines the detecting and tracking stages with efficient computation. The corresponding algorithms mainly include manifold collective density definition

density-based manifold collective clustering

and hierarchical collectiveness merging process. In the first process

a new manifold distance metric is presented to express the intrinsic characteristics of moving individuals. A collective density with novelty is defined based on this topological structure to describe the collectiveness between an individual and its neighbors. This collective density represents the local density efficiently

reflects the global consistency

and is highly adaptive to reveal the underlying patterns of varying densities in coherent motion. We propose a novel collective clustering to find a local topological relationship that can precisely recognize the collectiveness relationship between points and their surroundings. Similar to the core idea of fast search and density peak cluster method

the cluster peak centers of subgroups are assumed to be characterized by a higher collective density calculated by collectiveness than their neighbors. The density-based manifold collective clustering approach is proposed to detect local and global coherent motions with arbitrary shapes and varying densities. Three salient properties must not be ignored:1) the accurate identification of outliers and exploration of manifold structure

2) the automatic decision of group number without involving any arbitrary threshold

and 3) the capability of dealing with crowd scenes with varying densities. Thus

in the proposed method

the center of collective subgroups is characterized by two criteria:one is a higher collective density than its neighbors

and the other is a relatively large distance and inconsistent orientation from points with a higher density. From this view

the clustering method can determine the cluster centers automatically. In the recognition of global consistency part

inspired by the main ideology of the BIRCH clustering method

a hierarchical collectiveness merging strategy is developed to combine local motions and recognize global consistency. In this process

the collectiveness is used to capture the intracorrelations of subgroups

and local clusters are successfully combined into global coherent groups by merging the highly consistent pairwise subgroups iteratively.

Result

2

Collective Motion Database is employed to evaluate the experimental performance

and four state-of-the-art group detection techniques are used for comparison

namely

coherent filtering

collective transition

measuring crowd collectiveness

and collective density clustering. Results show that in complex scenes under different conditions

the experimental results of the algorithm remain highly effective and robust. Compared with the traditional clustering methods and state-of-the-art algorithms

average difference (AD) and variance (VAR) are used as criteria to evaluate the algorithm performance. The AD rate of our proposed method is controlled within 0.81

and the VAR rate is under 0.99

which is approximately 6% lower than those of classical clustering methods. Compared with such classical methods

our method exhibits great improvement. Accurate and effective recognition results can be obtained in crowd scenes with complex manifold structures and arbitrary density conditions

which solve the shortcomings of the classical methods in this special scenario.

Conclusion

2

This study proposes a cluster aggregation clustering algorithm based on manifold density for multiple complex real-world videos based on the manifold description and analysis of existing methods for identifying the lack of accuracy and stability of collective behavioral flow in manifold structures. Experiments on various real-world videos and comparisons with previous works validate that our method yields substantial boosts over state-of-the-art competitors. Therefore

the proposed algorithm can have a preferable adaptive performance in complex scenes with varying densities and arbitrary shape situations.