Drone-based Object Counting by Spatially Regularized Regional Proposal Network

ICCV 2017  ·  Meng-Ru Hsieh, Yen-Liang Lin, Winston H. Hsu ·

Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Proposal Networks (LPNs) and spatial kernels to simultaneously count and localize target objects (e.g., cars) in videos recorded by the drone. Different from the conventional region proposal methods, we leverage the spatial layout information (e.g., cars often park regularly) and introduce these spatially regularized constraints into our network to improve the localization accuracy. To evaluate our counting method, we present a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000 cars captured from different parking lots. To the best of our knowledge, it is the first and the largest drone view dataset that supports object counting, and provides the bounding box annotations.

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Datasets


Introduced in the Paper:

CARPK

Used in the Paper:

COWC PKLot

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Object Counting CARPK LPN Counting (2017) MAE 22.76 # 10
RMSE 34.46 # 9

Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Object Counting CARPK RetinaNet (2018) MAE 16.62 # 8
RMSE 22.30 # 8

Methods


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