## 1 Introduction
It is composed of an optimized, non-uniform Fire sub-network stack and a non-uniform sub-network stack of optimized SSD-based auxiliary convolutional feature layers for minimizing model size without negatively affecting object detection performance. In this article, we contribute to the body of research on object recognition in videos by proposing a two-tier system that couples motion detection with image classification. Unlike R-CNN systems that use static image features to find candidate regions, our system uses motion to extract possible object regions and does not extract any additional features from the identified regions with other ConvNets. Instead, motion detection functions as a class-agnostic object location method that generates a set of regions with possible objects. Each region is subsequently classified by a class-specific classifier (e.g., a ConvNet or an SVM) or an ensemble of classifiers such as a random forest (RF) [37]. Our system is agnostic to motion detection or image classification algorithms in that different motion detection and image classification algorithms can be coupled. In this investigation, we focus on designing and evaluating different ConvNet architectures to classify candidate regions identified by motion detection algorithms and compare their performance with RFs and linear SVMs. Specifically, we experimented with coupling three motion detection algorithms available in OpenCV 3.0.0 (KNN [38], MOG [39], and MOG2 [40]) with manually and automatically designed ConvNets, support vector machines, and random forests to classify objects in detected motion regions. We contribute to the body of research on EBM by experimentally demonstrating that computer vision, which has so far not been actively included in EBM systems (e.g., [4; 6; 7; 8; 9]), can be put to productive use in estimating omnidirectional bee traffic levels through bee motion counts. A long-term objective of this project is to develop an open source, open design, and open data citizen science platform of software tools and hardware designs for researchers, practitioners, and citizen scientists worldwide who want to monitor their beehives or use their beehives as environmental monitoring stations [41]. Our approach is based on the following four principles. First, the EBM hardware should be not only open design but also 100% replicable, which we ensure by using exclusively readily available off-the-shelf hardware components [42; 43]. Second, the EBM software should also be 100% replicable, which we achieve by using and building upon only open source resources. Third, the project's hardware should be compatible with standard beehive models used by many beekeepers worldwide (e.g., the Langstroth beehive [44] or the Dadadant beehive [45]) without any structural modifications of beehive woodenware. Fourth, the sacredness of the bee space must be preserved in that the deployment of EBM sensors should not be disruptive to natural beehive cycles. To ensure the replicability of our findings and to establish performance benchmarks for research communities, citizen scientists, and apiarists, we have made public our three manually labeled and curated image datasets (BEE1 [46], BEE2_1S [47], and BEE2_2S [48]) used in this investigation. We have also included in the Supplementary Materials the omnidirectional bee traffic videos on which we partially evaluated how closely the bee motion counts returned by the proposed system approximate the human bee motion counts. The remainder of this article is organized as follows. In Section 2, we describe the hardware and software of the BeePi system, our multi-sensor EBM monitor, where we implemented our two-tier method for counting bee motions to estimate levels of omnidirectional bee traffic in bee traffic videos from live beehives. In Section 3, we describe our experiments on training, testing, and validating different classifiers used in the second tier of our method. In Section 4, we present a preliminary evaluation of the whole system on several bee traffic videos.