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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Detection of Black Holes01:10

Detection of Black Holes

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Orthogonal Trajectories01:26

Orthogonal Trajectories

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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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Related Experiment Video

Updated: Jun 13, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Small-Target Ship Detection with Joint Spatio-Temporal Features Across Multiple Frames.

Ye Qian1,2,3, Zhen Hu1,2,3, Bo Zhang1,2,3

  • 1Taihu Laboratory of Deepsea Technological Science, Wuxi 214000, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Detecting small ships in challenging maritime infrared imagery is now improved. Our new framework enhances spatial features and analyzes temporal trajectories for robust small ship detection, significantly reducing false alarms.

Keywords:
multi-scale feature enhancementsea–sky background suppressionsmall-target ship detectiontemporal trajectory analysis

Related Experiment Videos

Last Updated: Jun 13, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Signal Processing

Background:

  • Maritime surveillance faces challenges in detecting small ships using infrared imagery due to sea clutter, clouds, and islands.
  • Limited spatial information in long-range infrared images complicates target identification.

Purpose of the Study:

  • To develop a robust detection framework for small ship targets in complex sea-sky backgrounds using infrared imagery.
  • To enhance target detection performance by integrating multi-scale spatial feature enhancement and temporal trajectory analysis.

Main Methods:

  • A multi-scale differential histogram of oriented gradients (HOG) method was used for candidate target extraction, enhancing target responses and suppressing background edges.
  • Log-spectrum-based saliency maps were fused with HOG features to improve target contrast and reduce clutter.
  • Inverse optical flow matching and Kalman filtering were employed for candidate trajectory extraction and association, utilizing temporal consistency.
  • A multi-feature trajectory filtering strategy, including motion entropy stability and peak signal-to-noise ratio, was designed to distinguish true targets from false alarms.

Main Results:

  • The proposed method achieved an average Background Suppression Factor (BSF) of 45.2 and an average Signal-to-Clutter Ratio Gain (SCRG) of 22.3 × 10³.
  • Receiver Operating Characteristic (ROC) analysis showed a mean detection rate exceeding 90% at a false-alarm rate of 10⁻³ across eight maritime infrared sequences.
  • Demonstrated substantial performance improvement over all baseline algorithms in complex maritime environments.

Conclusions:

  • The integrated framework effectively addresses challenges in small ship detection within cluttered maritime infrared imagery.
  • The combination of enhanced spatial features and temporal trajectory analysis provides a robust solution for maritime surveillance.
  • The proposed method significantly improves detection accuracy and reduces false alarms, offering enhanced robustness in complex environments.