1,496 research outputs found
Body-Part Joint Detection and Association via Extended Object Representation
The detection of human body and its related parts (e.g., face, head or hands)
have been intensively studied and greatly improved since the breakthrough of
deep CNNs. However, most of these detectors are trained independently, making
it a challenging task to associate detected body parts with people. This paper
focuses on the problem of joint detection of human body and its corresponding
parts. Specifically, we propose a novel extended object representation that
integrates the center location offsets of body or its parts, and construct a
dense single-stage anchor-based Body-Part Joint Detector (BPJDet). Body-part
associations in BPJDet are embedded into the unified representation which
contains both the semantic and geometric information. Therefore, BPJDet does
not suffer from error-prone association post-matching, and has a better
accuracy-speed trade-off. Furthermore, BPJDet can be seamlessly generalized to
jointly detect any body part. To verify the effectiveness and superiority of
our method, we conduct extensive experiments on the CityPersons, CrowdHuman and
BodyHands datasets. The proposed BPJDet detector achieves state-of-the-art
association performance on these three benchmarks while maintains high accuracy
of detection. Code is in https://github.com/hnuzhy/BPJDet.Comment: accepted by ICME202
BPJDet: Extended Object Representation for Generic Body-Part Joint Detection
Detection of human body and its parts (e.g., head or hands) has been
intensively studied. However, most of these CNNs-based detectors are trained
independently, making it difficult to associate detected parts with body. In
this paper, we focus on the joint detection of human body and its corresponding
parts. Specifically, we propose a novel extended object representation
integrating center-offsets of body parts, and construct a dense one-stage
generic Body-Part Joint Detector (BPJDet). In this way, body-part associations
are neatly embedded in a unified object representation containing both semantic
and geometric contents. Therefore, we can perform multi-loss optimizations to
tackle multi-tasks synergistically. BPJDet does not suffer from error-prone
post matching, and keeps a better trade-off between speed and accuracy.
Furthermore, BPJDet can be generalized to detect any one or more body parts. To
verify the superiority of BPJDet, we conduct experiments on three body-part
datasets (CityPersons, CrowdHuman and BodyHands) and one body-parts dataset
COCOHumanParts. While keeping high detection accuracy, BPJDet achieves
state-of-the-art association performance on all datasets comparing with its
counterparts. Besides, we show benefits of advanced body-part association
capability by improving performance of two representative downstream
applications: accurate crowd head detection and hand contact estimation. Code
is released in https://github.com/hnuzhy/BPJDet.Comment: 15 pages. arXiv admin note: text overlap with arXiv:2212.0765
Advances in Object and Activity Detection in Remote Sensing Imagery
The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms
Rethinking Object Detection in Retail Stores
The convention standard for object detection uses a bounding box to represent
each individual object instance. However, it is not practical in the
industry-relevant applications in the context of warehouses due to severe
occlusions among groups of instances of the same categories. In this paper, we
propose a new task, ie, simultaneously object localization and counting,
abbreviated as Locount, which requires algorithms to localize groups of objects
of interest with the number of instances. However, there does not exist a
dataset or benchmark designed for such a task. To this end, we collect a
large-scale object localization and counting dataset with rich annotations in
retail stores, which consists of 50,394 images with more than 1.9 million
object instances in 140 categories. Together with this dataset, we provide a
new evaluation protocol and divide the training and testing subsets to fairly
evaluate the performance of algorithms for Locount, developing a new benchmark
for the Locount task. Moreover, we present a cascaded localization and counting
network as a strong baseline, which gradually classifies and regresses the
bounding boxes of objects with the predicted numbers of instances enclosed in
the bounding boxes, trained in an end-to-end manner. Extensive experiments are
conducted on the proposed dataset to demonstrate its significance and the
analysis discussions on failure cases are provided to indicate future
directions. Dataset is available at
https://isrc.iscas.ac.cn/gitlab/research/locount-dataset.Comment: Information Erro
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