Smart Timelapse Video Bandwidth Reduction via Neural Network Event Detection
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Solution Overview
Problem
Conventional internet-connected/cloud-enabled cameras require significant bandwidth and storage for continuous video recording, as they upload full video streams regardless of event occurrence, leading to inefficient use of resources and high costs.
Innovation Solution
Implementing a smart timelapse video system on a camera device using a neural network for event detection, which adjusts video frame rates based on detected events, encoding only relevant frames for upload, thereby reducing bandwidth and storage usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If continuous video upload is implemented, then complete video data is available for review, but bandwidth consumption and storage usage increase significantly
Solution Approach 1:
The system extracts only the essential information (detected objects and events) from the continuous video stream using neural network analysis, rather than uploading the entire video data. This allows selective transmission of critical moments while discarding redundant continuous footage, thereby reducing bandwidth consumption while preserving important video information.
Solution Approach 2:
The system dynamically changes the video upload parameter from continuous full-resolution upload to event-triggered selective frame upload. When events are detected (object appearance, motion, sound), the system switches to uploading high-frame-rate video segments; during normal periods, it uploads only timelapse frames, thus adapting bandwidth usage to actual content importance.
2Loss of information
If full video stream is uploaded continuously, then all captured content is stored, but storage costs and bandwidth usage increase
Solution Approach 1:
The neural network extracts key events and objects from the continuous video stream, identifying which segments contain meaningful content worth storing. Only these extracted event segments are uploaded and stored in full detail, while non-event portions are either discarded or stored as low-resolution timelapse sequences, dramatically reducing total data volume while preserving all important content.
Solution Approach 2:
The video stream is segmented into event segments and non-event segments based on neural network detection. Event segments (containing detected objects, motions, or sounds) are uploaded at full frame rate and resolution, while non-event segments are represented by sparse timelapse frames or not uploaded at all, creating a segmented storage strategy that optimizes both data completeness and storage efficiency.
3Loss of energy
If fixed frame rate timelapse is used, then bandwidth is reduced, but important events may be missed
Solution Approach 1:
The system implements dynamic frame rate adjustment based on detected events. During normal periods without detected objects or motions, the system uses low frame rate timelapse to conserve bandwidth. When the neural network detects events (object appearance, motion triggers, sound events), the system dynamically switches to high frame rate capture for those specific segments, ensuring important events are captured with sufficient temporal resolution while maintaining overall bandwidth efficiency.
Solution Approach 2:
The neural network continuously analyzes video frames and provides feedback about detected events, which then controls the video capture and upload rate. When events are detected, the feedback triggers increased frame capture rate and immediate upload of surrounding video segments. This closed-loop feedback mechanism ensures that bandwidth is allocated dynamically based on actual content importance, preventing missed events while avoiding unnecessary uploads during calm periods.
4Productivity
If cloud server processing is used, then timelapse video can be generated, but CPU cycles and processing power are consumed
Solution Approach 1:
The system performs preliminary action by conducting object detection, motion detection, and event classification using neural networks on the camera device itself before video upload. This pre-processing identifies which video segments contain important content, allowing the cloud server to skip expensive transcoding operations on irrelevant footage and focus only on processing and storing event-containing segments, thereby reducing overall CPU cycle consumption across the system.
Solution Approach 2:
The camera device performs self-service by executing neural network-based event detection and classification locally, autonomously determining which video segments warrant upload and storage. This local intelligence eliminates the need for the cloud server to analyze every uploaded frame to identify important content, significantly reducing the cloud server's CPU workload and energy consumption while maintaining high productivity in generating event-focused timelapse videos.
Data Source
AI summary
An apparatus including an interface and a processor. The interface may be configured to receive pixel data generated by a capture device. The processor may be configured perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, determine whether the classification of the objects corresponds to an event, generate a full video stream in response to all of the video frames and generate encoded video frames. The full video stream may be recorded to a storage medium local to the apparatus. The encoded video frames may be communicated to a cloud service. The encoded video frames may comprise a first sample of the video frames selected at a first rate when the event is not detected and a second sample of the video frames selected at a second rate while the event is detected.