Oleg Zabluda's blog
Monday, September 19, 2016
 
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning (2016) T.
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning (2016) T. Nathan Mundhenk et al (LLNL)
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We have created a large diverse set of cars from overhead images, which are useful for training a deep learner to binary classify, detect and count them. [...] The set contains contextual matter to aid in identification of difficult targets. We demonstrate classification and detection on this dataset using a neural network we call ResCeption. This network combines residual learning with Inception-style layers and is used to count cars in one look. This is a new way to count objects rather than by localization or density estimation.
[...]
It has recently been demonstrated that one-look methods can excel at both speed and accuracy [19] for recognition and localization. The idea of using a one-look network counter to learn to count has recently been demonstrated on synthetic data patches [20] and by regression on subsampled crowd patches [21]. Here we utilize a more robust network, and demonstrate that a large strided scan can be used to quickly count a very large scene with reasonable accuracy.
[...]
The cost of running GoogLeNet is 30k ops per pixel at 224x224 [...] Table 7. Performance results taken for our models running on Caffe on a single Nvidia GeForce Titan X based GPU [...] The AlexNet version will count cars at a rate of 1 km^2 per second. A company such as Digital Globe which produced satellite data at the rate of 680,000 km^2 per day in 2014 would theoretically be able to count the cars in all that data online with 8 GPUs. [Orbital Insight] claimed that they can count cars in 4 trillion pixels worth of images in 48 hours [...] our AlexNet based solution would be able to count that many pixels in 23 hours using one single GPU.
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https://arxiv.org/abs/1609.04453
https://arxiv.org/abs/1609.04453

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