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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| """This module defines the base classes and structures for object tracking in YOLO.""" | |
| from collections import OrderedDict | |
| import numpy as np | |
| class TrackState: | |
| """ | |
| Enumeration class representing the possible states of an object being tracked. | |
| Attributes: | |
| New (int): State when the object is newly detected. | |
| Tracked (int): State when the object is successfully tracked in subsequent frames. | |
| Lost (int): State when the object is no longer tracked. | |
| Removed (int): State when the object is removed from tracking. | |
| """ | |
| New = 0 | |
| Tracked = 1 | |
| Lost = 2 | |
| Removed = 3 | |
| class BaseTrack: | |
| """ | |
| Base class for object tracking, providing foundational attributes and methods. | |
| Attributes: | |
| _count (int): Class-level counter for unique track IDs. | |
| track_id (int): Unique identifier for the track. | |
| is_activated (bool): Flag indicating whether the track is currently active. | |
| state (TrackState): Current state of the track. | |
| history (OrderedDict): Ordered history of the track's states. | |
| features (list): List of features extracted from the object for tracking. | |
| curr_feature (any): The current feature of the object being tracked. | |
| score (float): The confidence score of the tracking. | |
| start_frame (int): The frame number where tracking started. | |
| frame_id (int): The most recent frame ID processed by the track. | |
| time_since_update (int): Frames passed since the last update. | |
| location (tuple): The location of the object in the context of multi-camera tracking. | |
| Methods: | |
| end_frame: Returns the ID of the last frame where the object was tracked. | |
| next_id: Increments and returns the next global track ID. | |
| activate: Abstract method to activate the track. | |
| predict: Abstract method to predict the next state of the track. | |
| update: Abstract method to update the track with new data. | |
| mark_lost: Marks the track as lost. | |
| mark_removed: Marks the track as removed. | |
| reset_id: Resets the global track ID counter. | |
| """ | |
| _count = 0 | |
| def __init__(self): | |
| """Initializes a new track with unique ID and foundational tracking attributes.""" | |
| self.track_id = 0 | |
| self.is_activated = False | |
| self.state = TrackState.New | |
| self.history = OrderedDict() | |
| self.features = [] | |
| self.curr_feature = None | |
| self.score = 0 | |
| self.start_frame = 0 | |
| self.frame_id = 0 | |
| self.time_since_update = 0 | |
| self.location = (np.inf, np.inf) | |
| def end_frame(self): | |
| """Return the last frame ID of the track.""" | |
| return self.frame_id | |
| def next_id(): | |
| """Increment and return the global track ID counter.""" | |
| BaseTrack._count += 1 | |
| return BaseTrack._count | |
| def activate(self, *args): | |
| """Abstract method to activate the track with provided arguments.""" | |
| raise NotImplementedError | |
| def predict(self): | |
| """Abstract method to predict the next state of the track.""" | |
| raise NotImplementedError | |
| def update(self, *args, **kwargs): | |
| """Abstract method to update the track with new observations.""" | |
| raise NotImplementedError | |
| def mark_lost(self): | |
| """Mark the track as lost.""" | |
| self.state = TrackState.Lost | |
| def mark_removed(self): | |
| """Mark the track as removed.""" | |
| self.state = TrackState.Removed | |
| def reset_id(): | |
| """Reset the global track ID counter.""" | |
| BaseTrack._count = 0 | |