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Features act as a key factor in pedestrian detection task.
In order to adapt the FCN to pedestrian detection task, we fine-tune it with bounding boxes labels.
The improved performance indicates that the proposed neural features are applicable to pedestrian detection task, due to their strong representation.
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Experiments were conducted under homogeneous and heterogeneous scenarios and the proposed HMCA showed increased accuracy rates for pedestrian detection and image classification tasks.
We show the generalization of our object representation architecture by applying it to undertake various tasks, i.e. pedestrian detection and action recognition.
Most of the current approaches for pedestrian detection using moving cameras treat the problem as a recognition task: a foreground detection is followed by a recognition step to verify the presence of a pedestrian.
Extensive experiments are conducted on car detection task in realistic environments and well recognized public pedestrian detection dataset (INRIA dataset).
In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand.
So that's called the detection task.
Engineers experiment with the pedestrian detection system on a test track.
But the Night Vision system with pedestrian detection is the acme of superfluous options.
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