Highway Vehicle Counting in Compressed Domain

Liu, Xu; Wang, Zhong Lin; Feng, Jiashi; Xi, Hongsheng

doi:10.1109/cvpr.2016.329

Cited by 29 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other categories. Other object categories have also been considered in the context of counting, due to their social or economical impact, such as vehicle counting [15], [27], [30], [41] or animal counting [1], [43], [46]. In this work, we do not focus on any individual object category, but rather, aim at proposing a category independent counting approach.…”

Section: A Counting Specific Objectsmentioning

confidence: 99%

Divide and Count: Generic Object Counting by Image Divisions

Stahl

Pintea

Gemert

2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

We propose a general object counting method that does not use any prior category information. We learn from local image divisions to predict global image-level counts without using any form of local annotations. Our method separates the input image into a sets of image divisions -each fully covering the image. Each image division is composed of a set of region proposals or uniform grid cells. Our approach learns in an endto-end deep learning architecture to predict global image-level counts from local image divisions. The method incorporates a counting layer which predicts object counts in the complete image, by enforcing consistency in counts when dealing with overlapping image regions. Our counting layer is based on the inclusion-exclusion principle from set theory. We analyze the individual building blocks of our proposed approach on Pascal-VOC2007 and evaluate our method on the MS-COCO large scale generic object dataset as well as on three class-specific counting datasets: UCSD pedestrian dataset, and CARPK and PUCPR+ car datasets.

show abstract

Section: A Counting Specific Objectsmentioning

confidence: 99%

Divide and Count: Generic Object Counting by Image Divisions

Stahl

Pintea

Gemert

2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…The task of vehicle counting is to estimate the number of vehicles presented in a given image [20]. The existing vision-based vehicle counting approaches can be divided into three categories: counting by detection, counting by clustering and counting by regression [21]. In general, the clustering [22] or regression [23] methods need to explicitly extract the object feature in order to build an accurate appearance model.…”

Section: Related Workmentioning

confidence: 99%

Real‐time vehicle detection and counting in complex traffic scenes using background subtraction model with low‐rank decomposition

Yang

2017

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…Wang et al [20] and Liu et al . [21] combined a spatial regression and a local temporal regression for vehicle counting. These regression methods need to extract informative features and may fail in the dense scene.…”

Section: Related Workmentioning

confidence: 99%