Objective

2

Video-based object detection and tracking have always been a research topic of high concern in the academic field of computer vision. Video object tracking has important research significance and broad application prospects in intelligent monitoring

human-computer interaction

robot vision navigation

and other aspects. Although the theoretical research of video object tracking technology has made considerable progress and several achievements have entered the practical stage

research on this technology still faces tremendous challenges

such as scale change

illumination change

motion blur

object deformation

and object occlusion

which result in many difficulties in visual tracking

particularly object scale mutation within a short time. It will lead to the loss of tracking elements

and the accumulation of tracking errors will lead to tracking drift. If the object scale is consistent

then considerable scale information will be lost. Thus

scale mutation is a challenging task in object tracking. To solve this problem

this study proposes an adaptive scale mutation tracking algorithm (kernelized correlation filter_you only look once

KCF_YOLO).

Method

2

The algorithm uses a correlation filter tracker to realize fast tracking in the training phase of tracking and uses you only look once (YOLO) V3 neural network in the detection phase. An adaptive template updating strategy is also designed. This strategy uses the method of comparing the color features of the detected object with the object template and the similarity of the image fingerprint features after fusion to determine whether occlusion occurs and whether the object template should be updated in the current frame. In the first frame of the video

the object is selected

assuming that the category of the object to be tracked is human. The object area is stored as the object template

T

. The object is selected

and it enters the training stage. The KCF algorithm is used for tracking. KCF extracts the multichannel history of gradients features of the object template. In the tracking process

1.5 times of the object template is selected as the object search range of the next frame

considerably reducing the search range. Tracking speed is remarkably improved. When the frame number is a multiple of 20

it enters the detection stage and uses YOLO V3 for object detection. YOLO V3 identifies all the people (

P

1

P

2

P

3

…

P

n

) in the current frame image. All the people to be identified are compared with the object template stored before 20 frames

and their color and image fingerprint features are extracted and compared with similarity (the similarity selection image fingerprint algorithm and color features are combined). If the similarity is greater than the average similarity of the first 20 frames

then the object template will be updated to the person with the greatest similarity. Simultaneously

the scale of the tracking box will be updated in accordance with the YOLO detection to achieve scale adaptation; otherwise

the object is judged as occluded and the template will not be updated. In the tracking phase

the updated or not updated object template is used as the latest status of the object in the tracking process for subsequent tracking. The preceding steps are repeated every 20 frames until the video and tracking end. The color and phase features are complementary. The image fingerprint feature selects the perceptual Hash(PHash) algorithm. After the discrete cosine transformation

the internal information of the image is mostly concentrated in the low-frequency area

reducing the calculation scope to the low-frequency area and losing color information. The color feature counts the distribution of colors in the entire image. The combination ensures the accuracy of similarity. A total of 11 video sequences representative of scale mutation in the object tracking benchmark(OTB)-2015 dataset are tested to prove the effectiveness of the proposed method. The results show that the average tracking accuracy of this algorithm is 0.955

and the average tracking speed is 36 frames/s. The self-made data of object reproduction are completely occluded for 130 frames. The result shows that tracking accuracy is 0.9

proving the validity of the algorithm that combines kernel correlation filtering and the YOLO V3 network. Compared with the classical scale adaptive tracking algorithm

accuracy is improved by 31.74% on average.

Conclusion

2

In this study

we adopt the ideas of correlation filtering and neural network to detect and track targets

improving the adaptability of the algorithm to scale mutation in the object tracking process. The experimental results show that the detection strategy can correct the tracking drift caused by the subsequent scale mutation and ensure the effectiveness of the adaptive template updating strategy. To address the problems of the traditional nuclear correlation filter being unable to deal with a sudden change in object scale within a short time and the slow tracking speed of a neural network

this work establishes a bridge between a correlation filter and a neural network. The tracker that combines a correlation filter and a neural network opens a new way.