Combing spatial and temporal features for crowd counting with point supervision | IEEE Conference Publication | IEEE Xplore

Combing spatial and temporal features for crowd counting with point supervision


Abstract:

In this paper, we present a new approach to count the number of people that cross a counting line from video images. This paper focuses on point-level annotation in train...Show More

Abstract:

In this paper, we present a new approach to count the number of people that cross a counting line from video images. This paper focuses on point-level annotation in training images and incorporate spatial features along with novel temporal features in training the structured random forest for estimating crowd density. By computing the crowd velocity, we model the crowd counting map as elementwise multiplication of crowd density map and crowd velocity map. Integrating over crowd counting map on the line of interest(LOI) locations leads to the instantaneous LOI counting numbers. We show that results are comparable to those obtained when using more complex and costly techniques.
Date of Conference: 29 August 2017 - 01 September 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information:
Conference Location: Lecce, Italy

References

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