Abstract:
Abstract: Recently, some researchers have exploited computer vision based video analysis to monitor the growth status of crop under the natural condition. Since leaf is the largest organ of the vast majority of plants, it often serves as primary monitoring object. Most of algorithms of illness analysis detect the blobs on a leaf surface and then judge which kind of diseases. In a leaf image, the blobs may be caused by shadow, dust, highlight, etc., which is prone to confuse with the blobs caused by diseases. To accurately judge the illness of a leaf for online surveillance, it is important to consider the time factor, since the blobs caused by the aforementioned factors may disappear with time elapsing. There exist some reasons such as the various poses, mutual occlusion, appearance and the irregular movement, which make the conventional detection and tracking methods hard to locate the leaves accurately in the images. In this paper, a novel scheme to monitor the leaves of vine grape was proposed. To improve the accuracy of leaves detection, a traditional RGB image was replaced by a G/R image to train the deformable part model (DPM) since the former makes it easier to distinguish the grape leaves and background than the latter. The DPM detector for leaves was constructed based on HOG feature, which was a mixture over three components representing a different aspect of a leaf. Since high dimension of HOG feature hampered real-time detection, PCA method was exploited to reduce its dimension, which speeded up the process of training and detection effectively. By utilizing the trained model, the overall score was computed for each root location according to the best possible placement of the parts through the matching procedure. The scores were sorted and the detection with respect to the highest score was picked out. To robustly trace the sharp movement of a leaf, probability model based online object tracking algorithm with color features was put forward. In proposed algorithm, object-background model capable of differentiating a leaf from the background was constructed firstly. To reduce the risk of drifting towards regions which exhibit similar appearance of leaf (but not really leaf) at a next frame, then a distractor-aware representation was combined to the formal model to generate a discriminative object model. Based on this model, overlapping candidate hypotheses densely was sampled within the search region and computed both the vote and distance scores for each candidate. This allows us to efficiently obtain the new object location in the next frame. In the long term tracking process, detection repeated at 30 minutes intervals to check whether new leaves appeared in the vision field. For the sake of the robustness, the images were gathered at various conditions, such as sunny, cloudy, shadow, flowering stages and fruiting stages, to train the detection and tracking models. Experiments were conducted to evaluate the performance of leaf detection at five different setting. The experimental results showed that the average detection rate reached up to 90.13%, and average false detection rate fell down to 8.52%. For the tracking algorithm, results also were exciting: the overlap rate was as high as 0.85, and the average center error was 17.33. Compared with the classical KLT tracking algorithm, our algorithm demonstrated the better robustness in the condition of illumination change and sharp movement.