DeepSORT

DeepSORT (arXiv 1703.07402). Extends SORT with a Re-ID appearance metric. Confirmed tracks are matched first by cosine distance on appearance embeddings (with Mahalanobis gating), then any remaining tracks fall back to IoU matching. Best for long-term identity maintenance.

Unlike the other trackers, DeepSORT needs an appearance embedding per detection. In Rust you supply an AppearanceExtractor; in Python you pass embeddings directly.

Rust: implement an extractor

use trackforge::traits::AppearanceExtractor;
use trackforge::types::BoundingBox;
use image::DynamicImage;

struct MyExtractor;

impl AppearanceExtractor for MyExtractor {
    fn extract(
        &mut self,
        image: &DynamicImage,
        boxes: &[BoundingBox],
    ) -> Result<Vec<Vec<f32>>, Box<dyn std::error::Error>> {
        // Crop each box, run your Re-ID model, return one embedding per box.
        Ok(boxes.iter().map(|_| vec![0.0_f32; 128]).collect())
    }
}

use trackforge::trackers::deepsort::DeepSort;
let mut tracker = DeepSort::new(MyExtractor, 70, 3, 0.7, 0.2, 100);

Python: bring your own embeddings

from trackforge import DEEPSORT

tracker = DEEPSORT(max_age=70, n_init=3, max_iou_distance=0.7, max_cosine_distance=0.2, nn_budget=100)

detections = [([100.0, 100.0, 50.0, 100.0], 0.9, 0)]
embeddings = [[0.1] * 128]  # one vector per detection, from your Re-ID model

for track_id, tlwh, score, class_id in tracker.update(detections, embeddings):
    print(track_id, tlwh)

Parameters

Tuning: lower max_cosine_distance (~0.15) for stricter Re-ID; raise nn_budget if appearance drifts over time; lower n_init to 1 when detections are reliable and you need tracks immediately.