A simple and effective recognition system for indoor scenes is presented. The proposed system has two phases, namely, creation of mandatory and desirable objects and an indoor scene recognition system (ISRS). In the first phase, a list of probable objects and their classification, such as mandatory and desirable objects, for any generic scene is created based on real-time indoor environment clubbed with human knowledge on standard datasets. In the second phase, the proposed system contains four stages. In the first stage, the proposed ISRS identifies and recognizes the objects of the given key frame based on a simplified version of CNN architecture of YOLO v3. In the second stage, the identified objects are divided into two sets of mandatory and desirable objects with a simple dictionary look-up. In the third stage, the objects are identified to belong to a probable scene, and this technique is called scene-object identification. In the final stage, a binary scene representation (BSR) is proposed for each probable scene, and a final scene recognition is obtained with a new scene-score, obtained after converting the binary BSR into a decimal number. The effect of the proposed ISRS has been experimented with standard datasets and measured in terms of standard metrics, besides comparison with existing schemes. The results are encouraging.

中文翻译：

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基于具有简单编码方案的强制性和期望对象的自动室内场景识别

提出了一种简单有效的室内场景识别系统。所提出的系统有两个阶段，即创建强制性和理想对象以及室内场景识别系统（ISRS）。在第一阶段，基于在标准数据集上结合人类知识的实时室内环境，为任何通用场景创建可能对象及其分类的列表，例如强制性和理想对象。在第二阶段，提议的系统包含四个阶段。在第一阶段，提出的 ISRS 基于 YOLO v3 的 CNN 架构的简化版本识别和识别给定关键帧的对象。在第二阶段，通过简单的字典查找将识别出的对象分为两组强制对象和期望对象。在第三阶段，对象被识别为属于一个可能的场景，这种技术称为场景对象识别。在最后阶段，为每个可能的场景提出二进制场景表示（BSR），并使用新的场景分数获得最终场景识别，该分数是在将二进制 BSR 转换为十进制数后获得的。除了与现有方案进行比较之外，建议的 ISRS 的效果已经用标准数据集进行了试验，并根据标准指标进行了测量。结果令人鼓舞。除了与现有方案进行比较之外，建议的 ISRS 的效果已经用标准数据集进行了试验，并根据标准指标进行了测量。结果令人鼓舞。除了与现有方案进行比较之外，建议的 ISRS 的效果已经用标准数据集进行了试验，并根据标准指标进行了测量。结果令人鼓舞。