Behavior recognition is already starting to move towards some scenarios, although it needs to be more sophisticated technically.
According to the "2019-2024 Research Report on China's Machine Vision Industry Prospects and Investment Opportunities" released by the China Business Industry Research Institute, the scale of China's machine vision market exceeded 10 billion yuan for the first time in 2018; , the machine vision market will further expand,It is estimated that the machine vision market will reach nearly 12.5 billion yuan in 2019.
It is true that the market size of the CV (machine vision) industry is not small and profitable, but when technology products mature and start to be applied, how to eat this cake has become the biggest problem faced by many CV startups. . At the same time, continuous losses and profit pressure are also urging every CV company to “run”.
趋视科技并不属于CV领域最知名的行业，然而它们却在落地应用和盈利上先人一步，其公司创始人徐飙表示："If 90% of companies in the industry are losing money, we belong to the other 10%."
how do they do it
Figure | Xu Biao, founder of Truthvision Technology
CV not only has face recognition, but also behavior analysis
When it comes to CV, attention and topics are often concentrated in the field of face recognition. SenseTime, Megvii, etc. are the focus of attention both inside and outside the industry, but CV is not equivalent to face recognition, it also includes behavior recognition. Xu Biao introduced that since its establishment, Truthvision Technology has always aimed at behavior recognition.
“行为识别就是识别人类或者车的行为，Such as people's fighting behavior, car running red light behavior and so on. Although both belong to machine vision, face recognition and behavior recognition are two technologies and different fields. "
At the technical level, face recognition can be completed through a photo, while behavior recognition needs to be judged by combining continuous data, because behavior itself is a continuous and dynamic process.in short,Face recognition solves the problem of who the target is, and behavior solves what kind of thing.At present, behavior recognition is often used in judicial management, smart stores,intelligent
Xu Biao told us: "There are many fields where behavior recognition is applicable, but because the technology is not mature enough, it is difficult for behavior recognition technology to play a very good role in the face of too complex and non-standard scenes. Therefore, this technology can only It is first applied in some vertical scenarios, and gradually accumulated and improved in the process of application, so as to expand to more scenarios, and finally meet the requirements of human behavior cognition in a large range."
So what are the technical difficulties of behavior recognition?
Since behavior is diverse, it includes individual behavior and group behavior, and each behavior is expressed in different ways. For example, fighting and stealing, fighting between individuals and fighting between groups are completely different.Therefore, behavior recognition faces great difficulties at the data collection level, which mainly involve problems such as occlusion and dislocation.
At the same time, the angle of human viewing the world is three-dimensional, and the picture captured by the camera is two-dimensional, so there will be a person in the video showing an arm, but because the distance parameter cannot be collected in the video,所以遮挡、错位的现象会让AI算法难以判断。
其次学习数据欠缺。众所周知，许多AI技术依靠深度学习算法模型去训练，这导致要让AI实现行为识别，就必须先给行为下定义，让AI知道行为是什么。然而前面已经提到行为非常复杂，甚至很多时候AI需要学习判断的是负面行为，因此企业很难获取到大量的学习数据。而算法模型没有经过大量数据去训练，也就很难“聪明”起来，从而在识别的效果和精度上难以达到用户需求。
不过尽管在技术上需要更加精进，但行为识别已经开始走向一些场景。
CV企业破冰关键：规模化
徐飙介绍：“公司一开始关注的就是行业落地而非通用场景，且瞄准的第一个领域就是司法领域行业的管理，比如监狱管理，是否有犯人打斗、翻墙、攀爬等。这对于司法领域的管理而言是一个刚需，能够降低人力管理成本，提升管理质量。”
而行业落地和通用场景落地两条路径的最大区别，在徐飙看来，前者能够助力企业快速实现规模化落地，而这至关重要。
他谈到：“所有CV厂商在近年来特别强调落地，本质上就是规模化落地，即企业在一个项目试点实现技术落地后能够快速复制到下一个同类型的场景中，而不是做完一个试点，下一个场景再重新做一遍，这无疑增加了许多成本。”
对于企业而言，要实现规模化落地首先在最初寻找落地行业时，就要找到能够实现规模化、可复制性强的场景。其中的关键在于，企业对于用户核心诉求的把握是否精确。徐飙认为，CV企业要实现规模化必须了解用户的需求，所谓需求指的不仅是用户对于功能的需求，还包括用户对性能当中准确度的要求。
“这需要碰撞。有些时候没有人会告诉你他的需求和对准确度的要求是什么，企业往往需要通过试点、交流、反馈、修正......逐步形成一个行业共识，而并非单个客户的需求。”
但即便把握了用户需求和性能指标并不足够，企业还要评估自身的技术体系、优势能否满足用户的需求和指标。最后企业还要考虑实现规模化之后，是否会被竞品取代，这要求其必须在技术落地应用过程中打造自身的技术门槛，如此厂商们才能率先占领市场，并在后续的竞争中获胜。
回到趋视科技自身，徐飙谈道：“公司明年的短期计划，一方面是确保在司法行业实现规模化，创造更多的收益；同时也会将技术落地到智慧门店场景。小规模化带给我们盈利，也验证了技术已经达到可复制状态，所以我们将会向更大的市场进行布局。”
【镁客·请讲】栏目 策划&撰写：温暖