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People Detection and Tracking with BoT-SORT

Introduction

Accurately detecting and tracking people across video frames is essential for modern applications in retail analytics, security systems, and crowd management. Traditional tracking models struggle with identity preservation during occlusions and camera movement, that is, the same person receives different IDs when reappearing, making long-term behavior analysis difficult. In this blog, we will explore how BoT-SORT (Bag of Tricks SORT) performs as one of the modern tracking algorithms.



Problem Statement

Organizations implementing advanced person detection and tracking systems face critical challenges that ByteTrack and traditional methods cannot fully address:


Key challenges include:

Camera Motion Sensitivity: Moving cameras cause dramatic position shifts that break tracking consistency, leading to frequent ID switches

Suboptimal Kalman Filter Design: Traditional filters estimate aspect ratios instead of direct width/height, resulting in poor bounding box predictions during occlusions

Limited Association Strategies: Existing methods create trade-offs between detection accuracy and identity preservation

Dense Crowd Performance: ID switches occur frequently when people cross paths or during complex occlusion scenarios

Scale and Motion Variability: Fast-moving individuals and scale changes continue to challenge tracking robustness


These limitations prevent organizations from achieving the tracking accuracy needed for critical applications like security monitoring and detailed behavioral analytics.



How BoT-SORT Person Tracking Works

BoT-SORT is a state-of-the-art multi-object tracking algorithm that builds upon ByteTrack's foundation.


Core capabilities of BoT-SORT include:

Motion Modeling: Kalman filter with direct width/height tracking for bounding box predictions

Camera Motion Compensation: Automatic detection and correction of camera movement to maintain tracking consistency

Real-time Processing: Optimized for live video stream analysis with CUDA acceleration



Benefits of Implementation

  1. Robust Camera Motion Handling: Maintains tracking accuracy even with moving cameras, essential for mobile surveillance and handheld devices.

  2. Enhanced Crowd Performance: Better handling of dense scenarios through improved association algorithms and appearance modeling.

  3. Precise Localization: Direct dimension tracking provides more accurate bounding boxes, improving downstream analytics.



Real-World Applications

BoT-SORT-powered person detection and tracking systems are advancing capabilities across various industries:

Retail Analytics: Enhanced customer journey mapping with consistent identity tracking across complex store layouts and camera movements.

Security and Surveillance: Mission-critical monitoring with reduced false alarms and improved suspect tracking across multiple camera views.

Crowd Management: Advanced crowd flow analysis with better handling of dense scenarios and complex pedestrian interactions.

Workplace Analytics: Precise occupancy monitoring and space utilization analysis with improved accuracy in dynamic environments.

Smart Cities: Robust pedestrian counting and traffic flow analysis that maintains accuracy across varying camera conditions and weather.



Implementation

Model Used

Our BoT-SORT implementation leverages YOLO11s for person detection combined with BoT-SORT's advanced tracking algorithm:

class PersonDetector:
    def __init__(self, confidence_threshold=0.5):
        # Force CUDA if available for optimal performance
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        
        # Load the YOLO 11s model
        print("Loading YOLO 11s model...")
        self.model = YOLO('yolo11s.pt')
        self.model.to(self.device)
        
        self.confidence_threshold = confidence_threshold

CUDA check: Uses GPU (cuda) if available, otherwise falls back to CPU.

YOLO model loading: Loads a pre-trained YOLOv11s model optimized for person detection

Model to device: Transfers the model to the chosen device (CPU/GPU).

Confidence threshold: Sets the minimum confidence score for detected persons to be considered valid.


Real-Time Tracking Pipeline

The heart of the system lies in its advanced frame-by-frame processing capabilities:

def process_frame_with_tracking(self, frame):
    # Use BoT-SORT to track persons across frames
    results = self.model.track(
        frame, 
        classes=[0],  # Only track persons (class 0)
        conf=self.confidence_threshold, 
        device=self.device, 
        persist=True,
        verbose=False
    )

Breakdown:

• frame - The current video frame (image) being processed as numpy array input

• classes=[0] - Filters detection to only "person" class (0) from COCO's 80 object classes

• conf=self.confidence_threshold - Minimum confidence score (e.g., 0.5 = 50%) to filter weak detections

• device=self.device - Hardware for processing ('cuda' for GPU speed or 'cpu' for compatibility)

• persist=True - Maintains consistent tracking IDs across frames with BoT-SORT's enhanced algorithm

• verbose=False - Silences debug output for faster processing (True = detailed console logs)

Full code is available at:



Results

In the output video, we observe that when individuals pass by one another, their identification numbers get altered or exchanged. This represents a difficulty in human detection and tracking systems. To address this issue, one could explore alternative methodologies or implement a different algorithm.


From the overhead perspective, the performance shows some improvement regarding identity tracking. Some individuals located in the upper right and upper left regions are failing to be identified. Some people within a crowded group are not being recognized continuously.



Get Help When You Need It

Implementing advanced person detection and tracking systems with BoT-SORT requires expertise in computer vision, deep learning, and system optimization. Whether you are building retail analytics, security systems, or crowd management solutions, professional guidance can accelerate your development process and ensure optimal performance.


For Students and Researchers: Get assistance with BoT-SORT implementation, algorithm optimization, dataset preparation, and performance evaluation for academic projects comparing state-of-the-art tracking methods.


For Enterprises: Access production-ready BoT-SORT solutions including system architecture design, real-time deployment, scalability optimization, and custom feature development tailored to your specific use cases requiring the highest tracking accuracy.


Visit www.codersarts.com or email contact@codersarts.com to get expert support for your advanced people detection and tracking projects using BoT-SORT technology.



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