A multi-channel Haar-like feature based object tracking algorithm with multiple instance learning(MIL)is proposed in this paper. It overcomes the disadvantages of the MIL algorithm such as using limited information and not replacing weak features for color videos. First

in the original MIL algorithm

the color video frame is tracked with a single channel's information or by simply converting it to grayscale images. This may lose some feature information. Therefore

we propose that the target is represented with Haar-like features generated from three channels of RGB with completely random location

size and channel to represent the target better. Next

Haar-like features could not be replaced in the original MIL algorithm

which has difficulty reflecting the changes of the target and the background. Thus

we replace some weakest discriminative Haar-like features with new randomly generated Haar-like features when weak classifiers are selected. It introduces new information to the target model and adapts to the dynamic changes of the target appearance. The experiment on eight challenging color videos shows that the proposed method obtains optimal performance compared with the original multiple instance learning algorithm

weighted multiple instance learning algorithm

and distribution field based algorithm. It not only obtains the minimal average center location errors

but also obtains a higher average accuracy rate by 52.85%

34.75% and 5.71% than the other three algorithms. The proposed algorithm obviously promotes the tracking results compared with the original MIL algorithm on color videos by generating Haar-like features from three RGB channels and replacing some weakest discriminative Haar-like features in real time. It extends the application prospect of the MIL algorithm.