Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

8.8K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
8.8K
Classification of Systems-II01:31

Classification of Systems-II

547
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
547
Structural Classification of Joints01:20

Structural Classification of Joints

8.4K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
8.4K
Classification of Systems-I01:26

Classification of Systems-I

649
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
649
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Composted organic manures modulated soil-microbe interactions that enhanced the growth of tobacco by improving rhizospheric microbial structure and soil nutrients.

Microbiology spectrum·2026
Same author

Metabolomic analysis of Yunnan cigar tobacco leaves: impact of geography and climate on flavor characteristics and machine learning-based origin traceability.

Frontiers in plant science·2026
Same author

Neuromodulation of resting state brain network topography by heterolateral prefrontal transcranial photobiomodulation.

Neuroscience·2026
Same author

Comparative metabolomic of cigar tobacco leaves: A preliminary investigation of potential discriminatory metabolites between Yunnan and Foreign.

Biochemistry and biophysics reports·2026
Same author

4<i>l</i>-PBD: a probe beam deflection system with quadruple coupling length for acoustic detection.

Optics letters·2025
Same author

Topping influences crop photosynthesis and alters the absorption and redistribution of nutrients: a case study of tobacco.

Frontiers in plant science·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Mar 16, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

Rui Sun1, Guanghai Zhang2, Xiaoxing Yan3

  • 1School of Computer and Information, Hefei University of Technology, Tunxi Road 193, Hefei 230009, China. sunrui@hfut.edu.cn.

Sensors (Basel, Switzerland)
|August 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for pedestrian detection using hierarchical features and a weighted kernel sparse representation model. The approach improves autonomous vehicle safety by accurately identifying pedestrians in challenging outdoor conditions.

Keywords:
CENTRISTkernel methodpedestrian classificationpoolingsparse representation

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Related Experiment Videos

Last Updated: Mar 16, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Area of Science:

  • Computer Vision
  • Autonomous Systems

Background:

  • Pedestrian detection is crucial for autonomous vehicles to ensure safety.
  • Challenges include varying illumination, complex backgrounds, and occlusions in outdoor environments.

Purpose of the Study:

  • To propose a novel hierarchical feature extraction and weighted kernel sparse representation model for robust pedestrian classification.
  • To enhance the performance of pedestrian detection systems in autonomous vehicles.

Main Methods:

  • Hierarchical feature extraction using CENTRIST descriptor and max pooling for appearance invariance.
  • Kernel sparse representation model with a Gaussian weight function to handle occlusions.

Main Results:

  • The proposed method demonstrates robust performance on benchmark datasets (INRIA, Daimler) and real-world occluded datasets.
  • Achieved superior pedestrian classification compared to existing state-of-the-art methods.

Conclusions:

  • The novel model effectively addresses challenges in outdoor pedestrian detection.
  • The method offers a more robust solution for autonomous vehicle perception systems.