Development of AI System for Abandoned Object Detection

Abandoned Object Detection: From Motion Detection to Ownership Tracking

An abandoned object—a bag by a subway column, a box at a check-in counter, a backpack under a bus seat. The task seems simple until you face real traffic: thousands of frames per hour where an 'abandoned' item could be a shadow, static trash, or a person who momentarily sat down next to their belonging.

We, a team of AI engineers, specialize in developing such turnkey systems. Our experience includes projects for train stations, airports, and shopping centers. We guarantee recall > 90% with False Alarm Rate < 3 per hour per camera after calibration at your site. Contact us for a preliminary assessment of your project—we will analyze the requirements and propose an architecture.

Why Classical Motion Detection Fails

MOG2 and KNN background subtractors detect background changes, not the fact of abandonment. They produce FAR 50–100 events per hour at a busy point—security stops responding after a day of operation.

The real task is not to detect a static object, but to establish a cause-and-effect relationship: the object was with a person, the person left, the object remained.

How Ownership Tracking Solves the Problem

Instead of static thresholds, we introduce the concept of an owner. For each bag-like object (backpack, handbag, suitcase), we track whether a person was nearby. If the owner leaves and the object remains unmoved for a set number of frames—the system generates an alert. The key component is ownership_distance (in pixels, default 150 px). An architecture with ownership hysteresis is 40% more efficient than naive tracking by F1-score.

import cv2
import numpy as np
from ultralytics import YOLO
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class TrackedObject:
    obj_id: int
    bbox: list
    class_name: str
    last_owner_id: Optional[int]  # track_id of the person-owner
    frames_static: int = 0
    frames_unattended: int = 0
    is_abandoned: bool = False

class AbandonedObjectDetector:
    def __init__(self, model_path: str, config: dict):
        self.detector = YOLO(model_path)
        self.objects: dict[int, TrackedObject] = {}
        self.persons: dict = {}

        # Key thresholds—this is where the magic happens
        self.static_threshold = config.get('static_frames', 90)    # 3 sec @ 30fps
        self.unattended_threshold = config.get('unattended_frames', 150)  # 5 sec
        self.ownership_distance = config.get('owner_dist_px', 150)  # pixels

        self.bag_classes = ['backpack', 'handbag', 'suitcase',
                            'umbrella', 'sports ball']

    def _find_owner(self, obj_bbox: list,
                    person_tracks: list) -> Optional[int]:
        """Find the nearest person within ownership_distance"""
        obj_center = np.array([(obj_bbox[0]+obj_bbox[2])//2,
                                (obj_bbox[1]+obj_bbox[3])//2])

        min_dist = float('inf')
        owner_id = None

        for person in person_tracks:
            p_center = np.array([(person.bbox[0]+person.bbox[2])//2,
                                  (person.bbox[1]+person.bbox[3])//2])
            dist = np.linalg.norm(obj_center - p_center)
            if dist < min_dist and dist < self.ownership_distance:
                min_dist = dist
                owner_id = person.track_id

        return owner_id

    def process_frame(self, frame: np.ndarray) -> list[TrackedObject]:
        results = self.detector.track(frame, persist=True,
                                       classes=[0,24,26,28])  # person+bags
        abandoned = []

        persons = [r for r in results[0].boxes
                   if self.detector.model.names[int(r.cls)] == 'person']
        bags = [r for r in results[0].boxes
                if self.detector.model.names[int(r.cls)] in self.bag_classes]

        for bag in bags:
            bid = int(bag.id) if bag.id is not None else -1
            bbox = list(map(int, bag.xyxy[0]))

            if bid not in self.objects:
                self.objects[bid] = TrackedObject(
                    obj_id=bid,
                    bbox=bbox,
                    class_name=self.detector.model.names[int(bag.cls)],
                    last_owner_id=None
                )

            tracked = self.objects[bid]
            owner = self._find_owner(bbox, persons)

            if owner is not None:
                tracked.last_owner_id = owner
                tracked.frames_unattended = 0  # reset counter
            else:
                if tracked.last_owner_id is not None:
                    tracked.frames_unattended += 1

            # Object staticness
            prev_center = np.array([(tracked.bbox[0]+tracked.bbox[2])//2,
                                     (tracked.bbox[1]+tracked.bbox[3])//2])
            curr_center = np.array([(bbox[0]+bbox[2])//2, (bbox[1]+bbox[3])//2])
            if np.linalg.norm(curr_center - prev_center) < 5:
                tracked.frames_static += 1
            else:
                tracked.frames_static = 0

            tracked.bbox = bbox

            if (tracked.frames_static >= self.static_threshold and
                    tracked.frames_unattended >= self.unattended_threshold):
                tracked.is_abandoned = True
                abandoned.append(tracked)

        return abandoned

Why Ownership Hysteresis is a Key Component

One of the main sources of false alarms is a situation where a person puts down a bag, steps 2 meters away to grab coffee, and the system already considers the item abandoned. The solution is ownership hysteresis: the owner-item link is broken only if the distance exceeds the threshold for N consecutive frames, not at a single moment.

A second complex case: multiple people stand near an item, then all leave. You need to track last_owner_id with a history of the last 3–5 'owners'.

What is ownership_distance and How to Configure It?

ownership_distance (default 150 px) is the maximum Euclidean distance between the center of the bag and the center of the nearest person, within which the person is considered the owner. For large spaces (airport, stadium) the value is increased to 200–250 px, for narrow corridors decreased to 100 px. Tuning is done on a trace with manual annotation of abandonments.

Threshold Tuning for Different Scenarios

Case Study: Train Station, 12 Cameras (from Our Practice)

At one train station, we deployed naive static object detection—80+ false alarms per shift. The staff complained and began ignoring the system. After implementing ownership tracking with 3-second hysteresis and a minimum bbox size of 40×40 pixels (floor debris filter):

• FAR decreased from 80+ to 4–6 events per shift
• Recall on test set (30 staged abandonments): 93%
• Average time to alarm: 8 seconds after actual abandonment

Model: YOLOv8m, fine-tuned on 2400 images of station bags in various angles. Inference on NVIDIA T4 — 28ms per frame at 1080p. Savings on security FTE up to 70% due to automation. Project payback: 8–12 months.

What is Included in the Development of Abandoned Object Detection System

Each project includes:

• A trained YOLOv8m model, adapted to the types of baggage at your site.
• A collected dataset (at least 1000 annotated frames) with labels.
• Ownership tracking configuration with parameters for your scenario.
• REST API for integration with VMS and notification systems.
• API documentation and operator guide.
• 30-day warranty support after launch.

Implementation Stages

1. Site audit and collection of representative video samples.
2. Model fine-tuning (YOLOv8m/v8l) considering specific baggage classes.
3. Ownership tracking configuration per scenario (static_frames, unattended_frames, owner_dist_px).
4. Integration with VMS (Milestone, Genetec, Trassir) via RTSP and webhook.
5. Notification API (Telegram, e-mail, custom system).
6. 1-month warranty support after launch.

Development Timeline for Abandoned Object Detection System

We will assess your project and propose a solution. Get a consultation—write to us, discuss details. Order a pilot project on 1–4 cameras to verify the system's effectiveness at your site.