Deep Learning-based Real-time Video Processing
In this article, we explore how to build a pipeline and process real-time video with Deep Learning to apply this approach to business use cases overviewed in our research.
By Serhii Maksymenko, AI/ML Solution Architect at MobiDev
What is Serial Video Processing
Video data is a common asset which is used everyday, whether it is a live stream in a personal blog or security camera in a manufacturing building. A machine learning appliance is becoming a normal tool for processing video in a variety of tasks.
The video processing approach in this article is considered within the context of a personal data security application which was developed by the MobiDev Team.
In a nutshell, video processing can be seen as a sequence of operations done for each frame. Each frame includes processes of decoding, computation and encoding. Decoding is a conversion of the video frame from compressed format to the raw format. Computation is a certain operation which we need to do with the frame. And encoding is conversion of the processed frame back to the compressed state. This is a serial process. Although such an approach looks straightforward and easy to implement, it is not practical in most cases.
To make the above mentioned approach a workable one, it is necessary to set measurement criteria such as, speed, accuracy and flexibility. However, to get all these in one system might be impossible. Usually there is a trade-off between speed and accuracy in such cases. If we choose not to pay attention to speed, we’ll have a nice algorithm, but it will be extremely slow, and as a result, useless. If we simplify the algorithm too much, then the results may not be as good as expected. Consequently, a fast and accurate white elephant is impossible to integrate. The solution is supposed to be flexible in terms of input, output and configuration.
So, how to make the processing faster, while keeping the accuracy at a reasonably high level? There are two possible ways to solve the issue. The first one is to make the algorithms run in parallel, so they might be possible to keep using slower accurate models. The second one is to make a certain effort to accelerate the algorithms themselves or their parts with no significant loss of the accuracy.
Parallel Processing Method
The first approach is obvious. The video is split into parts and processed in parallel. This is a simple and straightforward way. It can be implemented with the help of video time pointers. In this method, splitting is virtual, and is not a real sub-file generation. However, in the output, we anyway have multiple video files which need to be combined into a single one. This makes the approach limited or at least uncomfortable when it comes to the tasks involved, especially using neighbouring frames for calculation. In order to deal with such a thing, we had to split the video with an overlap, so any information won’t be missed at joint places.
Because of this, it is impossible to deal with a stream from a web camera, for example. The file splitting approach enables processing of video files only. Besides, it may be difficult to pause, resume or even move the processing at a different position in timespin with this method. Despite the simplicity of the file splitting, it doesn’t meet the flexibility requirements.
Instead of splitting the video, the pipeline approach is aimed to split and parallelize the operations, which are performed during the processing. Basic operations for the usual video pipeline are decoding, computation and encoding. At least these three can be done in a concurrent way.
Why is the pipeline approach more flexible? One of the benefits of the pipeline is the ease with which you can manipulate the components due to requirements. Decoding can work by using a video file and encode frames into another file. That’s a common case.
Alternatively, input can be RTSP streamed from an IP camera. Output can be through a WebRTC connection in the browser or mobile application. There is a unified architecture which is based on a video stream for all combinations of input and output formats.
The computation part of the process is not necessarily an atomic operation. It can be logically split into sub-operations which can create more pipeline stages. It is not always reasonable however, to create too many components because multiprocessing latency may fade out the gain in performance.
Basically, the pipeline looks like exactly what we need in terms of flexibility. That’s not only because of changing the input and output format, but also because we can stop and continue the processing at any time.
Optimizing Pipeline Components
To understand what needs to be optimized, it is necessary to observe the time each stage of the pipeline takes. That way, it is easier to find out where the bottleneck is. The slowest pipeline stage defines the output FPS. It may be boring to do the measurements, but they can be automated without very much effort. The pipeline is using a queue for data exchange. Therefore, there will be a certain waiting time in the components. Usually, we need to write down how long each operation takes, how long each operation waits for the data from the previous component, and how long each operation waits for its result to send to the next component. Having this information simplifies performance analysis a lot.
Decoding and Encoding
Decoding and encoding can be done in 2 ways: software and hardware. Hardware acceleration is possible due to the decoder and encoder installed in NVIDIA cards in addition to CUDA cores. Here is a list of options which can be used for implementing the hardware acceleration of decoding and encoding.
- Compile OpenCV with CUDA support
- Compile FFmpeg with NVDEC/NVENC codecs support
- Use GStreamer with NVDEC/NVENC codecs support
- Use NVIDIA Video Processing Framework
Decoding and Encoding
Compiling from the source may be non-trivial, but it should be straightforward if you follow the instructions carefully. This can be done in an isolated environment such as a docker container, so that it is easy to deploy it on other machines or in cloud instances and you do not need to repeat the compiling procedure each time.
Compiling OpenCV with CUDA can optimize not only decoding, but also other calculations in the pipeline if they are performed using OpenCV. However, they will need to be written in C++ I suppose, because Python wrapper has no solid support for it. But that will be worth it in situations where you’d like to perform both decoding and numeric calculations on a GPU without copying from CPU memory.
Custom installation of FFmpeg and GStreamer allows using a built-in NVIDIA decoder and encoder. It is suggested going with FFmpeg first, because that should be easier in terms of maintenance. Moreover, many libraries use FFmpeg under the hood, so replacing it will automatically improve these libraries' performance.
Python wrapper gives a possibility to decode a frame right into a PyTorch tensor on GPU so that we can start inference without an extra copying from CPU to GPU.
- Use Mixed Precision technique
- Resize before inference
- Batch inference
The customers do not always have a GPU in the list of their hardware. Processing using a neural network is rare on a CPU, but it is still possible with certain limitations. It’s not appropriate to make large models for this purpose but more lightweight models will work. For example, in our work, we were able to get 30 FPS with an SSD object detection model on the CPU. Latest versions of Tensorflow and PyTorch are optimized for running certain operations on multiple cores and they can be controlled with the help of parameters. So, the absence of GPU doesn’t mean that the mission is impossible. For instance, this can be the case if you are dealing with cloud computing with limited resources.
But of course, a GPU is a must if you want a good model to work at your desired speed. We also applied certain techniques in order to speed-up GPU inference even more. In those cases, if your GPU supports fp16, it will be simple to apply mixed precision, which is part of the latest versions of PyTorch and TensorFlow. It enables use of fp16 precision for certain layers and fp32 for the rest, keeping the numerical stability of the network and therefore keeping the accuracy. An alternative and even more efficient way of model acceleration is TensorRT conversion. It is a more challenging method, but it can give 5x faster inference. In addition, there are other obvious optimization options such as resizing and batch inference.
How the Pipeline Approach Can Be Implemented in the Application
The flexibility of the system was essential in this case, because we aimed to process not only video files, but also different formats of video live-stream. It showed a good FPS in the range of 30-60, depending on the configuration used.
Additionally, we applied some other improvements: interpolation with tracking, sharing memory between processes, and adding several workers for the pipeline component.
Interpolation with tracking
You may be thinking, do we really need to make computation for each single frame? No, it is possible to skip frames with a certain interval, assuming that bonding boxes will be interpolated with the help of a tracking algorithm. An algorithm can be simple. For example, matching by the distance between centroids. That will be enough to have a pretty good interpolation, and therefore much better FPS.
In the tracking stage, this is essential to use more complex tracking algorithms or correlation tracker. But they really impact the speed if there are too many faces on the video. For example, if it is a crowded street video, then we cannot afford the tracking algorithm to be too complex.
Since we skip some frames, we’d like to know what the quality of the interpolated frames is. Therefore, we calculated the F1 metric and ensured that we don’t have too many false positives and false negatives because of interpolation. F1 value was around 0,95 for most of the video examples.
The next point is sharing memory between processes. An app can keep sending data through the queue but that was really slow. Alternative ways of sharing memory between processes in Python are tricky. Here are some of them:
- SharedMemory in Python 3.8
The PyTorch version of multiprocessing has the ability to pass a tensor handle through the queue so that another process can just get a pointer to the existing GPU memory. However, we decided to use another approach: system level shared memory. This method uses a special folder as a link to a space in RAM memory. There are libraries which provide a Python interface for using this memory. It drastically improved the speed of interprocess communication. Python 3.8 API for shared memory is another possible approach.
- Compare latency
- Take into account CUDA streams
- Correct the order of frames
Finally, we need to add several workers for a pipeline component. That’s something good to consider, but it is not always working correctly. We did that for the face detection stage. However, the same can be done for any computationally heavy operation which doesn’t require an ordered input. The thing is, that it really depends on the operations which are done inside. In cases where we had comparatively fast face detection, we got lower FPS after adding more detection workers. The time which is required for managing one more process can be bigger than the time we gained from it. Then, neural networks which are used in the multiple workers, will calculate tensors in a serial CUDA stream unless a separate stream is created for each network, which may be tricky.
One more technical issue which comes from multiple workers, is that due to their concurrent nature, they don’t guarantee the order to be the same as in the input sequence. Therefore, it requires additional effort to fix the order in the pipeline stages where it is important (for example, encoding). The same problem actually happens in the case of skipping frames. Anyway, with a certain configuration, multiple workers gave an improvement as well.
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