Computer Vision Developer

Job description Warehouse Theft Prevention System Advanced Computer Vision Solution for Inventory Security Project Overview Objective: Implement a computer vision-based system to monitor workers at warehouse entry/ exit points Purpose: Prevent unauthorized item removal by detecting clothing and accessory changes Focus: Identify subtle clothing swaps (e.g., local denim vs. branded items) Benefits: Reduce inventory shrinkage, maintain security without invasive searches, protect company assets System Architecture Core Components: 1. Hardware Layer High-resolution security cameras Worker identification system Entry/exit gates with integrated Cameras Edge computing devices 2. Software Layer Person identification & tracking Clothing & accessory segmentation Material & texture analysis Brand/style recognition Entry/exit image comparison Alert generation system Technology Stack Hardware: • 4K/8K resolution cameras with consistent lighting • GPU-accelerated edge computing devices • RFID/badge readers for worker identification • Enterprise-grade servers for data processing • Secure network infrastructure • Display terminals for security personnel Software: • Programming Languages: Python • Computer Vision Frameworks: OpenCV, TensorFlow • Deep Learning Models: CNN, ResNet • Database:S3 Buckets, MySQL/MongoDB • Backend: FastAPI • Frontend: React.js Key Technical Approaches 1. Appearance Matching •Person Re-Identification (ReID) technology •Clothing color, pattern, and style recognition from Multi Angle •Accessory detection and matching •Comprehensive body area segmentation •Multi-angle appearance analysis 2. Texture & Material Recognition •Fine-grained material classification •Brand-specific pattern recognition •Fabric texture analysis using specialized CNNs •Reflection and light response analysis •Pattern matching for logo and design detection 3. Object Detection & Tracking •Carried item identification and tracking •Item association with specific individuals •Temporal analysis of possessions •Anomaly detection for new/different items Workflow Diagram • Worker identified at entry point • Comprehensive appearance scan stored • Same process repeated at exit • AI system compares entry and exit appearances • Alerts generated for significant discrepancies • Security personnel review and respond to alerts • AI Methodology & Approaches Deep Learning Models: • Siamese Networks for comparing entry/exit images • Feature Extraction CNNs for material and texture analysis • Mask R-CNN for precise clothing segmentation • ReID Networks for person re-identification • Brand Classification Models for logo and brand detection Detection Capabilities Clothing Recognition: • Jacket and outerwear changes • Shirt and top replacements • Pants and bottoms substitutions • Footwear changes and modifications Material Differentiation: • Standard vs. premium denim • Generic vs. branded items • Similar-looking but different quality fabrics • Counterfeit vs. authentic items Accessory Tracking: • Bags and backpacks • Hats and headwear • Jewelry and watches • Footwear and Shoes Required Information for System Refinement • Warehouse Entry/Exit Policy Requirements • Current Procedures • Physical Layout Details • Uniform Policy Requirements • Acceptable Variations: • Visual Database Needs: • Special Considerations (if Any) • Alert System & Response Protocol 1. Warehouse Entry/Exit Infrastructure Complete layout of all entry and exit points Current flow of personnel through entry/exit points Available space for equipment installation at checkpoints Existing security camera placements and specifications Lighting conditions at all entry/exit areas 2. Personnel Management Information Current worker identification system (badges, cards, biometric) Shift change patterns and peak traffic times Total number of workers and daily throughput at each entry/exit Current check-in/check-out procedures Uniform policies and standard worker attire Allowed personal items and