SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection

Object tracking is one of the most challenging problems in the field of computer vision. In challenging object tracking scenarios such as illumination variation, occlusion, motion blur and fast motion, existing algorithms can present decreased performances. To make better use of the various features of the image, we propose an object tracking method based on the self-adaptive feature selection (SAFS) algorithm, which can select the most distinguishable feature sub-template to guide the tracking task. The similarity of each feature sub-template can be calculated by the histogram of the features. Then, the distinguishability of the feature sub-template can be measured by their similarity matrix based on the maximum a posteriori (MAP). The selection task of the feature sub-template is transformed into the classification task between feature vectors by the above process and adopt modified Jeffreys’ entropy as the discriminant metric for classification, which can complete the update of the sub-template. Experiments with the eight video sequences in the Visual Tracker Benchmark dataset evaluate the comprehensive performance of SAFS and compare them with five baselines. Experimental results demonstrate that SAFS can overcome the difficulties caused by scene changes and achieve robust object tracking.

DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

Autonomous harvesters can be used for the timely cultivation of high-value crops such as strawberries, where the robots have the capability to identify ripe and unripe crops. However, the real-time segmentation of strawberries in an unbridled farming environment is a challenging task due to fruit occlusion by multiple trusses, stems, and leaves. In this work, we propose a possible solution by constructing a dynamic feature selection mechanism for convolutional neural networks (CNN). The proposed building block namely a dense attention module (DAM) controls the flow of information between the convolutional encoder and decoder. DAM enables hierarchical adaptive feature fusion by exploiting both inter-channel and intra-channel relationships and can be easily integrated into any existing CNN to obtain category-specific feature maps. We validate our attention module through extensive ablation experiments. In addition, a dataset is collected from different strawberry farms and divided into four classes corresponding to different maturity levels of fruits and one is devoted to background. Quantitative analysis of the proposed method showed a 4.1% and 2.32% increase in mean intersection over union, over existing state-of-the-art semantic segmentation models and other attention modules respectively, while simultaneously retaining a processing speed of 53 frames per second.