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End-to-end visual pipeline for analyzing movement trajectories from raw pixel data to actionable spatial insights for autonomous systems, robotics, and smart environments.

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Spatial Trajectory Analytics

Modern telemetry systems capture where and how an entity moves. But turning those XY time‑series into decisions — collision avoidance, route optimisation, behavioural detection — requires a multi‑layer pipeline.

This dashboard walks through nine figures, each a transformation step that widens the lens from noisy coordinates to machine‑learning‑ready features.


Applications

  • Autonomous Vehicles: Real‑time trajectory overlap avoids path collisions and minimises idle taxiing
  • Smart Warehousing: Forklift telematics leverage percentile thresholds to trigger geo‑fenced slow‑downs
  • Public Safety & CCTV: Loitering detection uses directional histograms to flag suspicious circling
  • Aerial Robotics: Flight‑path redundancy informs battery allocation and rerouting

Dashboard

1. Frame‑Level Position Extraction

Frame-Level Position Extraction
Pixel‑accurate XY traces gathered at millisecond resolution. In autonomous vehicles and sports motion capture, this is the ground‑truth feed for higher‑order analytics.


2. Drift‑Free Trajectory Reconstruction

Drift-Free Trajectory Reconstruction
Smoothing and centring remove lens distortion and arena bias — the same principles that underpin SLAM correction in warehouse robots and navigation.


3. Multi‑Modal Time‑Series Context

Multi-Modal Time-Series Context
Velocity, speed and heading are fused into a stitched signal. In predictive maintenance, such composite traces anticipate anomalous wear before it escalates.


4. Directional Intent Polar Histogram

Directional Intent Polar Histogram
Rose plots bin heading into 30° wedges. Crowd‑flow controllers and CCTV triage engines use identical binning to forecast movement funnels in real time.


5. Velocity‑Gated Linear Strokes

Velocity-Gated Linear Strokes
Only high‑speed linear strokes survive this gate, isolating purposeful bursts from idle drift — mirroring how logistics firms flag forklifts exceeding safety thresholds.


6. Opposing Linear Paths by Orientation

Opposing Linear Paths by Orientation
Bidirectional segments reveal potential conflict corridors. Multi‑agent simulators plug these vectors into reinforcement learning for cooperative policy training.


7. Overlap Histogram by Direction

Overlap Histogram by Direction
Pairwise distances between opposing paths quantify redundancy within narrow angles — crucial for optimising patrol loops and warehouse pick routes.


8. Global Overlap Distribution

Global Overlap Distribution
The global 5th‑percentile cut‑off acts as a dynamic anomaly threshold. Telemetry dashboards surface paths below this line as potential process deviations.

9. Proximal Conflict Candidates

Proximal Conflict Candidates
The 5th‑percentile distance filter spotlights near‑misses. Insurance telematics and UAV traffic managers use the same threshold logic to price risk in real time.


Author: Umais Khan (2021)

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End-to-end visual pipeline for analyzing movement trajectories from raw pixel data to actionable spatial insights for autonomous systems, robotics, and smart environments.

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