Timetracker

Overview

Time tracking utilities for computer vision applications. Provides comprehensive time tracking for object detection workflows including total detection time, zone occupancy duration, and line crossing analysis. Supports both frame-based timing for video processing and real-time clock-based timing for live streams. Provides comprehensive time tracking capabilities including total detection time, zone occupancy duration, and line crossing analysis. Automatically adapts between frame-based timing (for video processing) and real-time clock-based timing (for live streams). The tracker maintains internal state for all tracked objects and provides both simple integration through detection object modification and detailed analytics through comprehensive statistics methods.

Class Overview

The TimeTracker class provides structured data management for timetracker operations.

Parameters

Optional[float]

Frame rate for frame-based timing. If None, uses system clock timing. Frame-based timing provides more accurate results for video processing with consistent frame rates. Range: > 0.0. Default is None (clock-based timing). Attributes:

Optional[float]

Frame rate used for timing calculations.

int

required

Current frame number (frame-based timing only).

bool

required

Whether using system clock (True) or frame-based (False) timing.

Dict[int, Union[datetime, int]]

required

First detection time for each tracker ID.

Dict[tuple, Union[datetime, int]]

required

Zone entry times keyed by (tracker_id, zone_id).

Dict[tuple, Union[datetime, int]]

required

Line crossing times keyed by (tracker_id, line_id, direction).

Examples

import cv2
import pixelflow as pf
from ultralytics import YOLO

# Basic setup with clock-based timing
model = YOLO("yolo11n.pt")
time_tracker = pf.timer.TimeTracker()

# Process video frame
frame = cv2.imread("frame.jpg")
outputs = model.track(frame)  # Enable tracking
results = pf.results.from_ultralytics(outputs)

# Update timing (modifies detections in-place)
time_tracker.update(results)
print(f"Detection times: {[d.total_time for d in results]}")

# Frame-based timing for video processing
time_tracker = pf.timer.TimeTracker(fps=30.0)
cap = cv2.VideoCapture("video.mp4")
while True:
ret, frame = cap.read()
if not ret: break
outputs = model.track(frame)
results = pf.results.from_ultralytics(outputs)
time_tracker.update(results)  # Automatically tracks frame-based timing

# Get detailed statistics for analysis
stats = time_tracker.get_detailed_stats(results)
total_times = stats['total']  # numpy array of total times
zone_times = stats['zones']   # dict of zone-specific times

# Reset specific trackers when objects leave scene
inactive_ids = [1, 3, 5]
time_tracker.reset(inactive_ids)

Notes

Detection objects are modified in-place with timing information
Frame-based timing requires consistent FPS throughout processing
Zone and line crossing data requires corresponding attributes in detection objects
Memory usage scales with number of unique tracker IDs over time
Use cleanup_inactive_trackers() periodically for long-running applications
O(n) time complexity where n is number of detections per frame
Memory usage grows with unique tracker count and zone/line complexity
Frame-based timing is more CPU efficient than datetime calculations

Getting Started

Detections

Annotators

Zones

Object Tracking

Processing Tools

Analytics

Overview

Class Overview

Parameters

Examples

Notes

​Overview

​Class Overview

​Parameters

​Examples

​Notes

Overview

Class Overview

Parameters

Examples

Notes