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Streamline Deep Learning for Video Analytics with DeepStream SDK 2.0
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- 1 Intro
- 2 IVA IN SMART CITIES
- 3 DEEPSTREAM SOFTWARE STACK
- 4 DEEPSTREAM 2.0
- 5 DEEPSTREAM BUILDING BLOCK Gstreamer Plugin
- 6 METADATA IN DEEPSTREAM
- 7 METADATA STRUCTURE (1/2)
- 8 DEEPSTREAM PIPELINE ARCHITECTURE
- 9 NVIDIA-ACCELERATED PLUGINS Some of the most commonly used
- 10 DECODER PLUGIN Name nvdec_h264, nvdec_H265
- 11 MULTI-STREAM BATCHING
- 12 VIDEO AGGREGRATOR
- 13 INFERENCE Name nvinfer
- 14 FORMAT CONVERSION & SCALING PLUGIN Name nvvidconv
- 15 OBJECT TRACKER
- 16 SCREEN TILER
- 17 ON SCREEN DISPLAY
- 18 AND MANY MORE PLUGINS
- 19 MEMORY MANAGEMENT Efficient Memory Management
- 20 MEMORY MANAGEMENT GPU to CPU copy
- 21 DEEPSTREAM REFERENCE APPLICATION Vehicle detection, tracking, classification
- 22 NVINFER CONFIGURATION FILE
- 23 DEEPSTREAM SINGLE STREAM OUTPUT
- 24 DEEPSTREAM REFERENCE APP OUTPUT
- 25 DEEPSTREAM REFERENCE APPLICATION System Configuration & Performance for 25x 720p streams
- 26 KPIs FOR PERFORMANCE
- 27 MOTIVATION
- 28 METHODOLOGY FOR PERFORMANCE ANALYSIS
- 29 THROUGHPUT MEASUREMENT
- 30 LATENCY MEASUREMENT USING GST-LOGS
- 31 PERFORMANCE BEST PRACTICES
- 32 CUSTOM PLUGIN FOR OBJECT DETECTION Implement custom TensorRT plugin layers for your network topology Integrate your TensorRT based object detection model in DeepStream
- 33 YOLO V2 OVERVIEW
- 34 DARKNET TO TENSORRT
- 35 TENSORRT PLUGIN FACTORY
- 36 CREATING APPLICATIONS WITH DEEPSTREAM Object detection and counting
- 37 DEEPSTREAM MODULAR APPLICATION
- 38 START DEVELOPING WITH DEEPSTREAM
