FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

ICCV, pp. 3687-3696, 2017.

Cited by: 80|Views32
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Abstract:

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations of existing methods a...More

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