Faster Than Real Time Stabilization of Smartphone Videos

Casual videos shot with a smartphone can often suffer from shakiness. ‎Software-based video stabilization methods have been actively developing over the ‎last years and can make such videos look much more professional and fluid. We ‎have just released Deshake, our video stabilizer app for iOS that can enable faster ‎than real-time video processing. In this article, we are going to ‎overview the video stabilization techniques available and discuss some of ‎engineering challenges we have faced during the development.‎

Video Stabilization

Generally speaking, every video stabilization algorithm has to implement two ‎steps:‎

  1. Analyse motion between the consecutive frames of the video. The sequence of such ‎motions will constitute a trajectory which is usually a pretty shaky one.‎‎
  2. Build a smoothed version of the trajectory and attempt to re-create the video as if it ‎were shot along this new trajectory.

The biggest problem here is that algorithm mostly has to operate with frames only. ‎Those are 2D images which are tremendously simplified representations of the real ‎dynamic 3D world. A lot could have happened in that world between two adjacent ‎frames. Even if we ignore the movements of the objects shot, the camera alone can ‎change its location, orientation or even some internal parameters like focal length or ‎zoom level.‎

To demonstrate why the problem is so arduous, let us ignore everything but the ‎camera movement for a moment. There are 6 degrees of freedom in the ‎movement already — 3 for location (x, y and z) and 3 for orientation (yaw, roll, pitch). ‎Do we have any chance to reconstruct them at least? Well, in a sense we cannot. It is well-known that based on only two 2D images of a static 3D ‎world taken with the same camera (no focal length modifications, etc.) you can’t ‎positively determine the relative movement of such camera between the shots. To ‎determine the movement you have to know some external data: it may be the ‎camera parameters (that’s why in 3D scanning and other applications calibrated ‎cameras are widely used) or some insights on the scene shot (for instance, if you have ‎shot a building you can solve the problem based on the assumption that its windows are rectangular and walls ‎are perpendicular to the ground).‎

Back to our case. We have much more than just two images and for a certain point ‎of view motion estimation does not necessarily require camera position estimation. ‎At this point, different stabilization algorithms diverge a lot.‎

The most complicated and reckless algorithms attempt to reconstruct the full 3D picture based on the ‎frames only. As we already know, even in a perfectly static world you cannot do this ‎with two adjacent frames: any reconstruction would necessarily include camera ‎movement estimation which is impossible as you know. However, three frames would be ‎sufficient (google up trifocal tensor, multi view 3D reconstruction and so on). But in ‎practice to accommodate dynamism of the scene and types of instability, such ‎algorithms have to account multiple consecutive frames, calibrate camera on the fly ‎and do some other non-trivial stuff. It is still quite unstable; for example, one known ‎weakness of such algorithms is camera rotations without movement. In such a case ‎there is almost no effect of parallax (i.e., relative shifting of objects positioned at different distances ‎from the camera) in the image and 3D reconstruction techniques may simply fail. It ‎is also extremely slow: probably the most notorious attempt in the field is Microsoft’s ‎First-Person Hyperlapse which spends minutes (!) on a single video frame. Certainly ‎there are some benefits in such an approach: by knowing real 3D positions of the camera you can easily draw a smooth physically correct alternative path in all 6 degrees of freedom. ‎Then, with a reconstructed 3D scenery you can literally render the stabilized video ‎as if it were really shot with a camera moving along that path.‎

The most conventional models pretend that the world is two-dimensional. For them ‎the camera is just a rectangular framework moving over an infinite plane. This may appear a highly simplistic approach, but in real life for each pair of adjacent frames ‎this simplification often works pretty well. From the mathematical point of view we can ‎call it the similarity motion model. With few degrees of freedom (4 with scaling, ‎otherwise 3) and very easy transform rules, it has proven itself robust and ‎computationally efficient. Moreover, you can physically interpret the trajectory you ‎get from this model: the camera is moving along a polyline on a plane, rotating here ‎and there. Such an interpretation, although it is not exactly reflecting the real world ‎situation, is a real helper at the smoothing step: it can even imitate camera motions ‎used by professional cameramen (see article Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths to learn more).‎


In fact, you don’t have to use this very planar model: there are other classes of ‎motions to choose from. For example, you can assume the world to be a sphere with ‎a camera in its center which can rotate between frames. Such a rotational model is ‎also often used in panorama stitching. This model can simulate some non-trivial ‎perspective transforms between frames that are out of scope for the similarity model.‎

‎So the overall idea is to leave frames in 2D, treating a single frame as a solid piece ‎of plane that can be moved around, scaled or even skewed, but still remains a ‎solid piece. The benefits of this approach are high computational speed and ‎robustness. The downside is that it is restricted to the allowed transforms.‎

There are also some intermediate solutions we can call 2.5D. The general idea is ‎that we track points as they move between frames, choose long enough and ‎consistent tracks and consider them, without any attempt to interpret what is ‎happening either in 3D or on the frames, as the motions of interest. Then we ‎smooth such tracks and try to bend each frame so that the points move from their ‎original positions to the smoothed tracks. The trick here is that we use, both during ‎smoothing and bending, the underlying ideas of true 3D reconstruction (e.g., ‎epipolar geometry), but without any actual 3D reconstruction. From the ‎performance viewpoint such algorithms are also somewhere between 2D and 3D.‎

Our main goal was to achieve at least real-time video processing on modern ‎smartphones, so for us there were no options but to stick to 2D. As for the motion ‎model, we use neither the similarity model nor rotational model, but some ‎experimental model of our own.‎

Motion Between Frames

As we have chosen how to stabilize motions, the question now is how to determine ‎the motions to be stabilized. The obvious way to go is to analyze frames. But how ‎exactly should this be done?‎

What we need here is called, in computer vision, Image Registration. Those are ‎techniques which align several images of the same scene. Generally speaking, such ‎techniques can be classified into the spatial domain methods and frequency domain ‎methods. Spatial methods compare pixel colours, while frequency methods first use ‎some tricky algorithms like Fast Fourier Transform to transform the array of pixels ‎into a set of waveforms and then look for correlations between them. The spatial ‎methods in turn can be classified into intensity-based and feature-based methods. ‎The intensity-based methods deal with intensity patterns of the whole image while ‎feature-based methods try to find correspondences between some local features ‎such as points, lines or contours. The feature-based methods are most widely used ‎for video stabilization today: they are faster as they don’t have to deal with the ‎whole images most of the time and they are more precise, as single features can be ‎aligned very accurately, sometimes up to subpixels.‎

There are some implementation variations of feature-based image registration, ‎but let’s focus on the approach that we have adopted. First of all, we use points as ‎features. We select a set of points on a frame and then try to determine what ‎locations those points are moved to on the next frame. Such movement of single ‎points on a frame is called the optical flow. The optical flow estimation is based on the assumptions that:

  1. patches of pixels around a point and its correspondence in ‎the next frame are almost identical and
  2. the point has probably not moved too far ‎between two adjacent frames.

So we compare the patches of pixels around ‎the point with those not far from it on the next frame and choose the one with the ‎greatest resemblance. To do it efficiently, we use the pyramidal variation of ‎Lucas-Kanade method.‎

There is still one important issue to solve, i.e., which set of points to choose as ‎features? It turns out that the best candidates for features are corners (i.e. vertices of ‎angles) on the image. The rationale is as follows: all points can be tentatively ‎classified into internal points, border points and corner points. If you choose an ‎internal point as a feature, not only a patch around the true image of that point on ‎the next frame will be similar to the original patch, but almost any of its neighbours ‎will suffice. For a border point, all of the true image neighbours along the border will ‎have confusingly similar patches in the next frame. But the corners are not affected ‎by such a problem. Interestingly enough, the fact that only corners are good to ‎determine the motion is not a deficiency of the particular algorithm we are using, in ‎absence of corners even the human brain can misidentify motions. This problem ‎is known in motion perception as the Aperture Problem.‎


So we need to find corners in the image. For this we use a variation of ‎Harris Corner Detector.‎

After we have selected the features on the image and found their correspondences ‎on the next frame, we have to deduce the motion between frames (i.e., the ‎transform). This step depends on the motion model chosen, but generally you just ‎optimize your model iteratively or in some other way to minimize the error between ‎your model’s prediction and real point movement just estimated.‎

That was the obvious way: you have frames, you analyze them. But there is an ‎alternative way also. What if you have some external sensor and its data is recorded ‎alongside the video? Certain attempts have already been done in this area. An ‎apparent choice for such a sensor is the accelerometer (and/or gyroscope) of a device. Most ‎modern smartphones already have it; it can provide surprisingly precise data at a ‎high rate (higher than the video frame rate) and it literally gives you the raw motion ‎between frames. There are solutions based on this approach, most notably ‎Instagram’s Hyperlapse. Pros are apparent: the biggest problem here is to calibrate ‎the accelerometer/gyroscope correctly (its timeline can differ a little from the camera’s ‎timeline, and it can have some drift). After that you get the required data almost for ‎free (from the computational point of view). The downside is that the only data you ‎have is the rotational component of the motion and you can say nothing of the ‎translational shaking or other types or movement like in shooting the moving target: ‎for instance, in shooting videos from a running car). Another clear downside is that ‎the video should be shot from a given app. So if you have shot it previously or want ‎to shoot it with another app to use some filters or exposure/focus controls, you just ‎won’t be able to stabilize the video.‎

Another curious example of using external sensors for motion estimation is the ‎recent attempts to use a depth camera (e.g. Kinect) alongside with a normal camera. ‎That way you have a 3D scene without the need to reconstruct it, which gives you ‎the pros of a fully 3D approach without most of its cons. The problem is that the ‎depth camera is not good at big distances (i.e., it is not applicable to outdoor ‎shooting yet). Other issues include calibration and ‎alignment of the colour camera with the depth camera. And, yes, the depth cameras are not sufficiently widespread yet.‎


As its name suggests, OpenCV is an open-source computer vision library. It ‎contains highly optimized implementations for many computer vision algorithms ‎‎(including some accelerated versions for CUDA, NEON, etc.). It is cross-platform ‎‎(written in C++) and has become a de-facto industry standard. Unsurprisingly, ‎OpenCV already contains a video stabilization implementation, accidentally it is ‎exactly the one we have just described. But an attempt to run this implementation as ‎it is has not been quite cheerful.‎

As a test case we have selected a 1-minute full HD video ‎captured with an iPhone at 30 frames per second. OpenCV’s native implementation has spent 13 minutes to ‎process this video, and a typical user won’t wait for so long. So we aimed at speeding-up ‎the processing 13-fold. Before discussing our results, let’s review ‎some details specific to the current OpenCV implementation.‎

First of all, OpenCV on iPhone runs totally on CPU. At the moment, OpenCV ‎supports only CUDA-based GPU computing, and it is not available on iPhone. ‎Moreover, the OpenCV implementation has been designed to be cross-platform, ‎and although some parts of the algorithm are optimized for specific platforms (for ‎example, video file decoding on iPhone uses Apple’s optimized AVFoundation framework), the ‎general pipeline had to remain suboptimal.‎

To be more specific, OpenCV stores single frames most of the time as RGB ‎encoded images (as it requires them either way to output stabilized frames), but the ‎computer vision algorithms (including feature detection, optical flow estimation, etc.) ‎require grayscale images as their input. That’s why OpenCV converts RGB frames ‎to grayscale each time it needs to pass them to individual steps: hence it performs ‎the grayscale conversion several times for each input frame. Another example: ‎besides the frame, the optical flow algorithm needs a series of auxiliary images ‎constituting what is called a Gaussian pyramid; this pyramid is also computed more ‎than once for each frame. ‎

So the first step for us was to remove such and similar redundancies from the ‎computations.‎

Our Solution

As I have already mentioned, OpenCV does grayscale conversion multiple times for ‎each frame. Yet the problem is deeper. In fact, during motion estimation (which is ‎run as a single-pass pre-processing through a video file) there is no single ‎algorithm in the pipeline that requires an RGB-encoded frame. But you may be surprised to ‎know that frames in video files are usually stored using the YUV colour space invented to transmit the colour television signal via the black-and-white ‎infrastructure. Its Y component represents luma (i.e. brightness) of the pixel and U, ‎V are responsible for chrominance (colour itself). Therefore, the problem is that ‎OpenCV first has to convert from grayscale (Y component) to RGB using additional ‎information (UV components) and then to convert it multiple times back to grayscale. ‎To eliminate it, but still maintain fast video decoding on iOS, you can use ‎AVFoundation in combination with Core Video’s pixel buffers. Intentionally ‎simplified setup code for asset reading with proper preparations may look like this:‎

Here the kCVPixelBufferPixelFormatTypeKey key tells AVFoundation to provide for ‎each frame a Core Video buffer and the format ‎kCVPixelFormatType_420YpCbCr8Planar is chosen to eliminate any unneeded colour ‎conversions during decoding. Note also that AVFoundation sets the ‎alwaysCopiesSampleData property of videoOutput to YES by default. It means that ‎each frame’s data is copied after decoding, which is not required in our case. So ‎setting this property to NO can sometimes give you a performance boost. The colour ‎format used here is planar, for us it means that the Y component will be stored in a ‎continuous region of memory separately from others, which makes processing we ‎have to do both simpler and faster.‎

Single frame reading can look like this:‎

It’s important to use CVPixelBufferGetBaseAddressOfPlane here instead of the standard ‎CVPixelBufferGetBaseAddress as the colour format is planar. If you are not familiar with ‎the Core Video framework, please note that in this example you can only process ‎pixels pointed by grayscalePixels before CVPixelBufferUnlockBaseAddress is called, and ‎you are not allowed to modify them (as the read-only access here was requested).‎

After we have removed all the unnecessary colour conversions and other ‎computations and did some other memory optimizations, the processing speed still ‎remained far from desired. So the next decision was to downscale frames before ‎processing. That may sound unreasonable, but the reality is that the full frame ‎definition gives you more noise than data for stabilization. We have downscaled ‎frames to half of their original size easily using cv::resize. It is the OpenCV ‎function already highly optimised, including even NEON support on iOS.‎

And that was still not enough. The next heuristic we have applied is as follows. ‎Feature detection is quite a computationally intensive algorithm; in fact, more than ‎‎60% of time at that moment was consumed by this step. But as we have estimated ‎the optical flow, we have already computed frame-to-frame feature mapping. So, if ‎on the previous frame the points were good enough to be considered features (they ‎had high ‘cornerness’), than probably after mapping they will still remain good. Why ‎not just reuse this knowledge? So, we can compute features on a given frame and ‎then do not recompute them from scratch for a few next frames: each time we can ‎use the points we get after optical flow estimation as features. So we have ‎keyframes, i.e., those for which we run the real feature detector, and all the other ‎frames get their set of features for free, as we compute the optical flow either way.‎

After all these optimizations, the processing time of a 1-minute video has decreased ‎from 13 minutes to 42 seconds. Well, that is already faster than real-time but still not ‎fast enough for us. In fact, all this time we consciously left one optimization in ‎reserve, that is, parallelization. As you certainly know, all modern iOS devices have ‎‎2-core CPUs, and this can potentially double your computational power at least if ‎you know how to use them efficiently. In fact, OpenCV already uses some ‎parallelization features, but for video stabilization the support is not extensive ‎enough.‎

Video stabilization has turned out to be an interesting problem in context of ‎parallelization. You can see that, for each frame we may want at some point to: read ‎it (that is, to request the next decoded frame from AVAsset reader), downscale it, ‎construct the Gaussian pyramid for it, find features on it (if it is a keyframe), compute ‎the optical flow (between it and the next one) — that’s 5 potential operations for a ‎single frame. The problem is not the count, but the interdependencies between ‎them, which you can see on the scheme:‎


The scheme shows the operations for a single frame in columns, while the ‎keyframes are marked with the darker background. The arrows connect an ‎operation with the dependent operations. In fact, the distance between keyframes in ‎our case is 8 rather than 6, but the general idea is the same. So, we have lots of ‎operations to do and we have to manage dependencies between them somehow: ‎NSOperationQueue is a perfect fit for the task. We apply a standard scheme: one ‎serial operation queue is used to coordinate the process (i.e., dispatch new ‎operations for the individual frames) and respond to requests from the user (i.e., begin ‎processing, cancel processing, etc.) and another concurrent operation queue is ‎used for all the frame operations. This works great. The only potential issue here is, as the video is processed a tail of processed dependencies is piled up: on the ‎scheme you can see that each compute optical flow operation has most of the ‎previous operations as its dependency in some generation. Such operations remain ‎in memory and can potentially mess some of your plans. However, you can easily ‎remove them if you apply the following code to already processed operations (for ‎example, it is sufficient to apply it to the keyframe read and flow operations):‎

This solution gives us an almost perfect parallelization: debug shows ~90% of the ‎CPU resources is consumed by our processing code and the other ~10% is used by ‎the system, probably for video decoding.‎

So after all these optimizations including parallelization, our test with a 1-minute full ‎HD video processing takes 22 seconds only.‎

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