In this paper, we present a new method that provides a substantial speed-up of person detection while showing high classification accuracy. Our method learns a Gaussian Mixture Model of locations and scales of the persons in the scene under observation. The model is learnt in an unsupervised way from a set of detections extracted from a small number of frames, so that each component of the mixture represents the expectation of finding a target in a region of the image at a specific scale. At runtime, the windows that most likely contain a person are sampled from the components and evaluated by the classifier. Experimental results show that replacing the classic sliding window approach with our scene-dependent proposals in state of the art person detectors allows us to drastically reduce the computational complexity while granting equal or higher performance in terms of accuracy.
Scene-dependent proposals for efficient person detection
Lisanti, Giuseppe;
2019
Abstract
In this paper, we present a new method that provides a substantial speed-up of person detection while showing high classification accuracy. Our method learns a Gaussian Mixture Model of locations and scales of the persons in the scene under observation. The model is learnt in an unsupervised way from a set of detections extracted from a small number of frames, so that each component of the mixture represents the expectation of finding a target in a region of the image at a specific scale. At runtime, the windows that most likely contain a person are sampled from the components and evaluated by the classifier. Experimental results show that replacing the classic sliding window approach with our scene-dependent proposals in state of the art person detectors allows us to drastically reduce the computational complexity while granting equal or higher performance in terms of accuracy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
