The goal of Object Counting task is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
Essentially, the CCNN is formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps.
However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods.
Ranked #2 on Object Counting on COCO count-test
Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.
Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17. 8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.
Ranked #1 on Object Counting on COCO count-test
Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i. e., the number of population can vary in [0, inf) in theory.
In this paper we present a large-scale vehicle detection and counting benchmark, named DroneVehicle, aiming at advancing visual analysis tasks on the drone platform.
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.