datitran/object_detector_app
Object Detector App
This application is a real-time computer vision system designed to identify and label objects within live video feeds, recorded files, and static images. It functions as a comprehensive framework that integrates pre-trained machine learning models with video processing pipelines to perform multi-object localization and visual data tracking.
The system distinguishes itself through a multithreaded architecture that decouples frame acquisition from detection logic, ensuring the interface remains responsive during continuous analysis. It provides specialized scripts for training and optimizing custom detection models, allowing users to improve accuracy for specific visual tasks. Furthermore, the application includes mechanisms for transmitting processed video frames to external network servers for remote monitoring and distribution.
Beyond its core detection capabilities, the software offers extensive performance tuning options, including adjustments for worker thread counts and frame resolution to balance processing speed and accuracy. The project is structured to support immediate inference using serialized model files, facilitating deployment without requiring additional configuration.
Features
- Real-Time Object Detection - Identifies and labels objects within live video feeds and recorded files using pre-trained machine learning models.
- Object Detection - Identifies and locates multiple distinct items within static images or video frames using pre-trained machine learning models.
- Video Stream Detections - Identifies and labels items within recorded video files by applying pre-trained models to sequential frames.
- Detection Model Training - Constructs and optimizes specialized object detection models using training scripts and distributed computing pipelines.
- Custom Vision Model Trainers - Includes a set of scripts for training and optimizing specialized detection models for visual recognition tasks.
- Inference Optimization Tools - Offers settings to adjust multithreading and processing parameters to balance detection speed and accuracy.
- Custom Vision Training - Provides specialized scripts for training and optimizing custom detection models to improve accuracy for specific visual tasks.
- Graph-Based Inference - Executes pre-trained machine learning models by traversing computational graphs to map input image tensors to localized object coordinates.
- Model Serialization - Loads pre-trained detection models from serialized binary files to enable immediate inference without additional configuration.
- Pre-trained Model Implementations - Executes pre-trained detection models on raw input data to identify objects immediately without requiring additional training.
- Video Frame Processing - Increases frame rates by offloading heavy detection tasks to parallel processes and background threads.
- Pipelines - Decouples frame acquisition from detection logic using concurrent queues to maximize throughput for real-time video analysis.
- Video Processing Frameworks - Implements a framework for capturing and analyzing video frames to perform high-speed object recognition and tracking.
- Video Stream Processing - Analyzes incoming video frames and transmits processed results to external network servers for remote monitoring.
- Live Video Stream Monitoring - Identifies and labels items appearing in live video feeds from connected cameras in real time.
- Streaming Inference Pipelines - Processes incoming video frames through a sequential pipeline that manages capture, inference, and output rendering in a synchronized loop.
- Real-Time Systems - Balances processing speed and detection accuracy through multithreading and parallel frame analysis in a real-time vision system.
- Worker Thread Patterns - Offloads heavy detection tasks to background threads to ensure the main interface remains responsive during continuous video analysis.
- Performance Tuning - Adjusts multithreading, worker counts, and frame resolution settings to balance processing speed and accuracy.
