"compaTAI presents a high-entropy Video Tracking Dataset captured in the chaotic heart of Mexico City (CDMX). While standard datasets (Waymo, nuScenes) are built on predictable environments, compaTAI focuses on "The Chaos Edge Case": the informal and non-linear traffic behaviors unique to Latin American megacities.

🌟 Why Video Tracking?

Unlike static image datasets, our video sequence provides temporal consistency. This allows models to train for Multi-Object Tracking (MOT) and Behavior Prediction, essential for navigating environments where lane markings don't exist and movement is erratic.

📊 Dataset SpecificationsTotal Duration: 52.06 Seconds.

Total Frames: 1,562 (at 30 FPS).
Resolution: High Definition (Processed for Fast-Start Streaming).
Annotation Format: Label Studio JSONL (Temporal Bounding Box Sequences).
Location 1: Mexico City (Critical Junctions).
Location 2: State of Mexico (Critical Junctions)
Type: 2D Video Rectangle Tracking with Interpolated Keyframes.

🏷️ The "Chaos" Taxonomy

Our labels capture the specific "Mexican Edge Cases" that standard sensors often misinterpret:

Label	Description	Why it matters
Pedestrian_Irregular	Pedestrians crossing mid-avenue or between cars	Predicts non-linear human trajectory.
Street_Vendor	Mobile vendors navigating active traffic lanes.	Unique obstacle detection for informal economies.
Infrastructure_Deficit	Missing signals, potholes, or zero lane markings.	Trains defensive driving and path planning.
Moto_Filtering	Motorcycles weaving between lanes at high speed.	High-frequency proximity detection.
Microbus_Stop	Public transport stopping in the middle of the road.	Predicts sudden traffic flow interruptions.

🛠️ Data Structure

The .jsonl file contains the temporal sequence of each object. Each annotation includes a sequence array tracking:frame: The exact frame number.x, y, width, height: Normalized coordinates (0-100) for resolution-independent training.time: Exact timestamp within the 52-second clip.

🚀 How to AccessYou can download the raw video and the JSONL metadata directly from this repository to start training your trajectory prediction models.

Python# Coming soon: compaTAI utility script to visualize trackingfrom datasets import load_dataset dataset = load_dataset("manuelvarale/compaTAI-CDMX-Chaos-Alpha")

💰 Commercial Version & Custom ServicesThis repository is a technical sample.

compaTAI offers specialized data for enterprise-grade autonomous systems:Full Video Datasets: 100+ hours of labeled CDMX traffic footage. Specific Edge Cases: Custom recordings of "Rainy Nights" or "Peak Hour at Indios Verdes". Custom Labeling: We use the compaTAI pipeline to label your raw data with our specialized taxonomy. Enterprise Licensing & Custom Data Requests: [[email protected]]

🔗 Visit our Hub: compatai.mx

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