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Moving object detection is an essential component for various applications of computer vision and image processing: pedestrian detection, traffic monitoring, security surveillance, etc. Though the latest moving object detection methods provide promising results, authentic detection is however tricky because of various challenges like illumination issues, occlusion, and background objects in an uncontrolled surround.

In this article, we discuss the almost common challenges of accurately detecting moving objects, give an overview of existing methods for detecting moving objects, and explain how your solution can be improved by applying deep learning.

Contents:

Methods for detecting moving objects

    one. Background subtraction and modeling

        Trajectory classification

    two. Temporal and spatial differencing

    3. Frame differencing

    4. Optical flow

seven disquisitional challenges in detecting moving objects

    1. Illumination challenges

    ii. Changes in the appearance of moving objects

    3. Presence of unpredicted motion

    4. Occlusion

    5. Complex backgrounds

    half-dozen. Moving shadows

    7. Camera problems

Solving moving object detection challenges with deep learning

    Convolutional neural networks

    Recurrent neural networks

    Deep neural networks

    Generative adversarial networks

    Deep learning vs traditional methods

Conclusion

Moving objects that should be detected in a video can be people, animals, or vehicles including cars, trucks, airplanes, and ships. Most of these objects are rigid: their shape doesn't change. Even so, there are also not-rigid objects, or objects that tin modify shape. People and animals constantly alter their silhouettes when doing actions and adopting poses. Other objects like waterfalls, hurricanes, clouds, and swaying copse also motility, simply they should exist considered by a detection algorithm as a office of the background.

A video consists of consecutive frames, and at that place are image processing techniques for detecting an object in each frame and then establishing relationships betwixt pixels in different frames to find objects that move. This type of video analysis includes the following four steps:

1. Feature signal classification
2. Moving object detection
3. Moving object tracking
4. Moving object analysis

Figure 1. The video analysis process

In this article, we'll consider only what methods are applied for moving object detection in a video image.

Methods for detecting moving objects

The first steps in video assay are detecting target objects and clustering their pixels. In this section, nosotros'll consider the post-obit approaches to detecting moving objects:

1. Groundwork subtraction
2. Temporal differencing
3. Frame differencing
4. Optical menses

Effigy 2. Traditional approaches to moving object detection

1. Background subtraction and modeling

Background subtraction, also known as foreground detection, is a oftentimes used method for segmenting motion in static scenes. By using mathematical modelling or probability theory, moving foreground objects are subtracted pixel past pixel from a static background epitome. The background image, or model, is created by averaging images over time, and the extracted foreground tin be used for object recognition. Below, you can see what techniques are applied to a video frame during background subtraction:

Figure 3. General concept of background subtraction

Imagine you lot have a room full of people in a video, and afterward implementing background subtraction, you're left with only people. Now y'all can work but with the people, which significantly simplifies farther object detection.

Though this method provides a good silhouette of objects, it's based on a static groundwork, so any changes in the image volition exist marked as foreground. In addition, the groundwork model should exist updated over time to adapt to dynamic scene changes. Several algorithms have been introduced to handle these challenges, including Mixture of Gaussians (MOG) and foreground partition, adaptive MOG, and a double Gaussian model.

Background subtraction algorithms for moving cameras tin be divided into two categories:

• Indicate trajectory-based methods track points to extract trajectories and cluster them according to motion similarity. These types of methods include approaches like trajectory classification.
• Spatio-temporal segmentation methods extend image segmentation to the spatial-temporal domain, where the spatial aspect determines semantic similarity over image space and the temporal attribute associates the move of object pixels over time. This means that nosotros should consider the spatio-temporal relationships of pixels to detect a moving object. However, many methods consider just the temporal attribute to find moving objects.

Trajectory nomenclature

Trajectory classification is a moving object detection method for moving cameras. This method includes such stages as choosing specific points in the first video frame and then obtaining a trajectory that represents continuous displacements at each indicate in adjacent frames.

Figure 4. General concept of a trajectory classification technique

In the end, a clustering approach is applied to classify the trajectories into background and foreground regions where moving objects can be detected.

However, a clustering approach faces difficulties in addressing points near the intersection of two subspaces. That's why region segmentation is practical for labeling regions with points that vest to neither the foreground nor the groundwork by comparison the region trajectories with the point trajectories. Nonetheless, the watershed algorithm used for saving the purlieus fragments often leads to deformation of an object's shape and profile.  Thus, point trajectory is non free from inaccurate trajectory classification or edge-preserving performance of moving objects.

two. Temporal and spatial differencing

Temporal differencing is one of the most popular approaches for detecting moving objects in video captured with a moving photographic camera. In contrast to detecting moving objects in video captured past a stable camera, there's no need to build a background model in accelerate, as the background is changing all the fourth dimension. The temporal differencing method detects the moving target by employing a pixel-wise difference method across successive frames.

Spatial differencing includes various approaches based on the semantic similarity of pixels across video frames. Thus, at that place tin can be a stable spatial relationship between current pixels and randomly selected pixels in the current frame. This approach became the ground of a detection algorithm that sets up a spatial sample set for each individual pixel and creates and defines a spatial sample difference consensus.

Recently, a new approach was introduced based on spatial filtering and region-based groundwork subtraction. The proposed spatial filter uses the spatial coherence effectually the pixel neighborhood of foreground regions. Information technology works not bad for removing noise and blurry parts of moving objects. This spatial filter can be easily extended to a spatio-temporal filter by including temporal neighbors.

Though the spatio-temporal sectionalization results are temporally consequent, these methods oftentimes take to deal with over-smoothing problems.

3. Frame differencing

The frame differencing arroyo is based on detecting moving objects by calculating the pixel-past-pixel difference of two sequent frames in a video sequence. This difference is so compared to a threshold to make up one's mind whether an object is in the background or foreground. This method has appeared highly adaptable to dynamic changes in the background, as it computes only the most recent frames. However, this approach also has some challenges to overcome. Peculiarly, it may inaccurately detect objects that move too fast or that all of a sudden finish. This happens because the last frame of the video sequence is treated as the reference, which is subtracted from the electric current frame.

Figure 5. Full general concept of frame differencing

Frame differencing with a reference frame is a modified temporal differencing method that was first introduced in 2017. Difference images are calculated by subtracting two input frames one at each pixel position. Instead of generating difference images using the traditional continuous frame differencing approach, this approach uses a fixed number of alternate frames centered around the electric current frame.

4. Optical period

The optical flow method uses the period vectors of moving objects over fourth dimension to detect them against the background.  For every pixel, a velocity vector is calculated depending on the direction of object movement and how quickly the pixel is moving across the image. Optical flow tin can as well be used for detecting both static and moving objects in the same frame. This approach is based on the post-obit principles of motility vectors:

• In-depth translation creates a set of vectors with a common focus of expansion.
• Translation at a abiding altitude is reflected as a range of parallel motion vectors.
• Rotation perpendicular to the view axis forms i or more sets of vectors first from straight line segments.
• Rotation at a constant distance leads to a variety of concentric motion vectors.

Figure 6. General concept of optical menses

This method has a loftier level of detection accurateness equally it copes even when the camera is shaking. Nonetheless, optical flow is time-consuming, as it requires computing the apparent velocity and direction of every pixel in a video frame. This method tin be used for existent-time moving object detection, but it's very sensitive to noise and may require specialized hardware.

Allow'due south encounter what other challenges can lead to inaccurate detection of moving objects.

7 disquisitional challenges in detecting moving objects

The challenges of detecting moving objects in a video depend on the environs where this video is captured and the camera used. A video captured indoors may contain shadows and sudden changes in illumination.

If a video is filmed outdoors, at that place are even more challenges, as the environs is uncontrollable. In this case, we ofttimes have to deal with complex backgrounds, abrupt motion, occlusion, and moving shadows. Besides, there can be movement-blurred objects or partial lens distortion if a video is captured with a moving camera.

Let'due south take a closer look at some of the nigh common challenges:

1. Illumination challenges

Sudden changes in lighting may lead to fake positive object detection. For example, indoors there may exist a sudden switching on or off of lights, or the light source might movement. Outdoors, there may be fast changes from brilliant sunlight to cloudy or rainy weather, shadows that autumn on moving objects, and reflections from bright surfaces. Additionally, there's always a risk that the background may have the same colour every bit a moving object.

That's why the groundwork model should exist adjustable to variations in illumination and precipitous changes of effulgence in order to avoid mistakes in detecting moving objects.

To deal with these challenges, researchers have offered a variety of solutions, including continuously updating background models, using local features of a moving object, and extracting Cepstral domain features.

Figure seven. Illumination challenge

2. Changes in the appearance of moving objects

All objects are three-dimensional in real life, and they may change their appearance when in motility. For case, the forepart view of a car is different from the side view. If objects are people, they may as well change their facial expressions or the wearing apparel they wear.  In addition, there can be non-rigid objects like human hands that change their shape over time. All of these changes to objects pose a challenge for object tracking algorithms. Various methods have been proposed to overcome this challenge. The near constructive are those that focus on tracking articulated objects:

• The adaptive appearance model is based on the Wandering-Stable-Lost framework. This model creates a 3D appearance using a mixture model that consists of an adaptive template, frame-to-frame matching, and an outlier process.
• Learning motility patterns with deep learning let the separation of contained objects using a trainable model that transfers optical menstruum to a fully convolutional network.

Figure 8. Appearance change challenge

3. Presence of unpredicted movement

When it comes to traffic surveillance, there's a problem of detecting objects with abrupt motility. For case, the jackrabbit first of a vehicle may cause a tracker to lose the object or cause an error in a tracking algorithm. Another source of detection issues are objects that motility too slow or too fast. If an object moves slow, the temporal differencing method will be unable to detect the portions of the object. With a fast object, in that location will be a trail of ghost regions behind the object in the foreground mask. Intermittent motion — when an object moves, and so stops for a while, and then starts moving once again — is also challenging.

Effigy 9. Precipitous motion

To overcome the claiming of unpredictable motility speed, researchers accept proposed such solutions equally:

• integrating the Wang-Landau algorithm
• introducing intensively adaptive Markov-concatenation Monte Carlo sampling
• integrating Hamiltonian dynamics
• and more

4. Occlusion

Occlusions can besides go far much more difficult to detect and rail moving objects in a video. For instance, when a vehicle drives on the route, it may get subconscious behind tree branches or other objects. Objects in a video stream may exist occluded fully or partially, which represents an additional claiming for object tracking methods.

Figure 10. Occlusion claiming

At that place are several ways you tin can bargain with occlusions:

• using an online expectation-maximization algorithm
• maintaining appearance models of moving objects over time
• considering information on the spatio-temporal context
• integrating a deformable part model into a multiple kernel tracker

5. Complex backgrounds

Natural outdoor environments may be likewise complex for many moving object detection algorithms. The reason for this is that the background may be highly textured or comprise moving parts that shouldn't be detected equally objects. For instance, fountains, clouds, waves, and swaying copse create irregular or periodic movements in the background. Dealing with such dynamics in the groundwork is challenging.

Figure 11. Circuitous groundwork

It'due south been suggested that this problem can be overcome with an auto-regressive model, applying a Bayesian decision rule to statistical characteristics of image pixels, or using an adaptive background model based on homography motion estimation.

six. Moving shadows

Nosotros've already mentioned that shadows may fall on moving objects, but what if objects themselves cast shadows? These shadows besides motility, and they're difficult to distinguish from the moving objects that bandage them. Particularly, they prevent further image processing activities similar region separation and classification that should come up after background subtraction.

Figure 12. Shadow challenge

Among the proposed methods for overcoming this problem are a modified Gaussian mixture model and a shadow emptying algorithm based on object texture and features.

7. Camera problems

In addition to object-related challenges, there are also issues related to camera limitations. Video may be captured by shaky cameras or cameras with low resolution or limited color data. As a result, a video sequence may contain block artifacts caused past compression or blur caused by vibrations. All these artifacts can misfile moving object detection algorithms if they aren't trained to deal with low-quality videos. A great variety of solutions have been proposed for overcoming camera challenges, simply this problem remains open.

Figure 13. Photographic camera issues

Solving moving object detection challenges with deep learning

Thanks to the availability of large video datasets like CDnet and Kinetics and deep learning frameworks like TensorFlow and Caffe, neural networks are beginning to be used for dealing with the challenges of moving object detection. In this department, we desire to testify you when to apply deep learning for object detection, and how diverse neural networks tin can be practical to eliminate drawbacks of moving object detection methods and overcome general challenges of detecting moving objects in a video sequence.

Convolutional neural networks

Convolutional neural network (CNN) models have already provided impressive results in image recognition. Their application to video processing has become possible by representing space and time as equivalent dimensions of the input information and performing 3D convolutions of both these dimensions simultaneously. This is accomplished by convolving a 3D kernel to a cube, formed by stacking multiple contiguous frames.

Figure fourteen. Visual comparison of 2D and 3D convolution

Using a CNN model for groundwork subtraction shows better performance for cameras with smooth motion in real-time applications. In addition, a pre-trained CNN model can work well for detecting the trajectories of moving objects in an unconstrained video.

Recurrent neural networks

Recurrent neural network (RNN) models combine convolution layers and temporal recursion. Long RNN models are both spatially and temporally deep, so they can be applied to various vision tasks involving sequential inputs and outputs, including detecting an object'due south activity that's deep in time.

An RNN can make utilize of the time-guild relationship between sensor readings, so these models are recommended for recognizing short object motions with a natural gild. In contrast, CNN models are better at learning deep features contained in recursive patterns, and then they can be applied for detecting long-term repetitive motions.

Long short-term retentiveness (LSTM) is an improved version of RNN that can non just regress and classify object locations and categories just also associate features to represent each output object. For example, an LSTM model combined with deep reinforcement learning tin be successfully applied for multi-object tracking in video. LSTM tin can also associate objects from different frames and provides great results in detecting moving objects in online video streams.

Deep neural networks

Groundwork subtraction based on deep neural network (DNN) models has demonstrated excellent results in extracting moving objects from dynamic backgrounds. This approach tin can automatically learn background features and outperform conventional groundwork modeling based on handcraft features.

In addition, DNN models can exist used for detecting anomalous events in videos, such as robberies, fights, and accidents. This can be accomplished past analyzing a moving object'south features by their velocity, orientation, entropy, and interest points.

Generative adversarial networks

Generative adversarial network (GAN) models have been applied to solve optical flow limitations to detection nearly motion boundaries in a semi-supervised fashion. This approach tin predict optical menses by leveraging both labeled and unlabeled data in a semi-supervised learning framework. A GAN can distinguish menstruation warp errors by comparison the ground truth flow and the estimated flow. This significantly improves the accuracy of flow estimation effectually motion boundaries.

Deep learning vs traditional methods

Considering what nosotros've just discussed, it's obvious that neural networks cope with moving object detection challenges improve than traditional algorithms. Permit'due south explain why.

• Deep learning performs ameliorate at video processing tasks by computing on more powerful resources: GPUs instead of CPUs.
• CNNs and improved models thereof have deeper architectures that ensure exponentially greater expressive capabilities.
• Deep learning allows for combining several related tasks; for example, Fast-RCNN can both observe moving objects and perform localization at the same time.
• CNNs and improved neural networks have a great capacity to learn, which allows them to recast object detection challenges as high-dimensional data transformation problems and solve them.
• Thanks to its hierarchical multi-stage structure, a deep learning model can reveal hidden factors of input data by applying multilevel nonlinear mappings.
• CNN models work better for tasks that include not only detection of moving objects but also nomenclature and selection of regions of interest.

Conclusion

Detecting moving objects in video streams is a promising notwithstanding challenging task for modern developers. Object detection in a video can be applied in many contexts — from surveillance systems to self-driving cars — to get together and analyze information so make decisions based on it.

In this article, we've underlined the challenges of detecting moving objects in video and have shown the limitations of existing detection methods. Fortunately, neural networks provide usa with many possibilities to improve the accuracy of moving object detection, every bit they provide admission to greater computational resources.

Read more about how AI can heighten your next projection below!

Source: https://www.apriorit.com/dev-blog/607-deep-learning-moving-object-detection-video

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